Next Point-of-Interest (POI) recommendation estimates user preference on POIs according to past check-in history, suffering from the intrinsic limitation of obtaining dynamic user preferences. Conversational Recommendation System (CRS), which can collect dynamic user preferences through conversation, brings a solution to the above limitation. However, none of the existing CRS methods consider the spatio-temporal factors in the action selection phase, which are essential for POI conversational recommendation. In this paper, we propose a new Spatio-Temporal Conversational Recommendation System (STCRS) to fuse the spatio-temporal and dialogue information for next POI recommendation. Specifically, STCRS first learns the spatio-temporal information in the user’s check-in history. Then reinforcement learning is used to decide which action (asking for an attribute or recommending POIs) to take at the next turn to achieve successful POI recommendation within as few turns as possible. Finally, our extensive experiments on two real-world datasets demonstrate significant improvements over the state-of-the-art methods.