diff --git a/docs/misc/changelog.rst b/docs/misc/changelog.rst
index 9e55d5059e..81081efb7d 100644
--- a/docs/misc/changelog.rst
+++ b/docs/misc/changelog.rst
@@ -37,7 +37,8 @@ Bug Fixes:
 - Fixed a bug in the ``close()`` method of ``SubprocVecEnv``, causing wrappers further down in the wrapper stack to not be closed. (@NeoExtended)
 - Fixed a bug in the ``generate_expert_traj()`` method in ``record_expert.py`` when using a non-image vectorized environment (@jbarsce)
 - Fixed a bug in CloudPickleWrapper's (used by VecEnvs) ``__setstate___`` where loading was incorrectly using ``pickle.loads`` (@shwang).
-- Fixed a bug in ``SAC`` and ``TD3`` where the log timesteps was not correct(@YangRui2015) 
+- Fixed a bug in ``SAC`` and ``TD3`` where the log timesteps was not correct(@YangRui2015)
+- Fixed a bug where the environment was reset twice when using ``evaluate_policy``
 
 
 Deprecations:
diff --git a/stable_baselines/common/evaluation.py b/stable_baselines/common/evaluation.py
index d43768e730..17f42cc7e4 100644
--- a/stable_baselines/common/evaluation.py
+++ b/stable_baselines/common/evaluation.py
@@ -1,17 +1,31 @@
+import typing
+from typing import Callable, List, Optional, Tuple, Union
+
+import gym
 import numpy as np
 
 from stable_baselines.common.vec_env import VecEnv
 
+if typing.TYPE_CHECKING:
+    from stable_baselines.common.base_class import BaseRLModel
+
 
-def evaluate_policy(model, env, n_eval_episodes=10, deterministic=True,
-                    render=False, callback=None, reward_threshold=None,
-                    return_episode_rewards=False):
+def evaluate_policy(
+    model: "BaseRLModel",
+    env: Union[gym.Env, VecEnv],
+    n_eval_episodes: int = 10,
+    deterministic: bool = True,
+    render: bool = False,
+    callback: Optional[Callable] = None,
+    reward_threshold: Optional[float] = None,
+    return_episode_rewards: bool = False,
+) -> Union[Tuple[float, float], Tuple[List[float], List[int]]]:
     """
-    Runs policy for `n_eval_episodes` episodes and returns average reward.
+    Runs policy for ``n_eval_episodes`` episodes and returns average reward.
     This is made to work only with one env.
 
     :param model: (BaseRLModel) The RL agent you want to evaluate.
-    :param env: (gym.Env or VecEnv) The gym environment. In the case of a `VecEnv`
+    :param env: (gym.Env or VecEnv) The gym environment. In the case of a ``VecEnv``
         this must contain only one environment.
     :param n_eval_episodes: (int) Number of episode to evaluate the agent
     :param deterministic: (bool) Whether to use deterministic or stochastic actions
@@ -20,17 +34,19 @@ def evaluate_policy(model, env, n_eval_episodes=10, deterministic=True,
         called after each step.
     :param reward_threshold: (float) Minimum expected reward per episode,
         this will raise an error if the performance is not met
-    :param return_episode_rewards: (bool) If True, a list of reward per episode
+    :param return_episode_rewards: (Optional[float]) If True, a list of reward per episode
         will be returned instead of the mean.
     :return: (float, float) Mean reward per episode, std of reward per episode
-        returns ([float], [int]) when `return_episode_rewards` is True
+        returns ([float], [int]) when ``return_episode_rewards`` is True
     """
     if isinstance(env, VecEnv):
         assert env.num_envs == 1, "You must pass only one environment when using this function"
 
     episode_rewards, episode_lengths = [], []
-    for _ in range(n_eval_episodes):
-        obs = env.reset()
+    for i in range(n_eval_episodes):
+        # Avoid double reset, as VecEnv are reset automatically
+        if not isinstance(env, VecEnv) or i == 0:
+            obs = env.reset()
         done, state = False, None
         episode_reward = 0.0
         episode_length = 0
@@ -45,13 +61,10 @@ def evaluate_policy(model, env, n_eval_episodes=10, deterministic=True,
                 env.render()
         episode_rewards.append(episode_reward)
         episode_lengths.append(episode_length)
-
     mean_reward = np.mean(episode_rewards)
     std_reward = np.std(episode_rewards)
-
     if reward_threshold is not None:
-        assert mean_reward > reward_threshold, 'Mean reward below threshold: '\
-                                         '{:.2f} < {:.2f}'.format(mean_reward, reward_threshold)
+        assert mean_reward > reward_threshold, "Mean reward below threshold: {:.2f} < {:.2f}".format(mean_reward, reward_threshold)
     if return_episode_rewards:
         return episode_rewards, episode_lengths
     return mean_reward, std_reward