Aiming at the randomness and uncertainty of the spatiotemporal distribution of electric vehicle (EV) charging loads, this paper proposes a forecasting method that takes into account the EV trip chain model. First, an improved trip chain model is constructed based on trip chain theory, and the required features are fitted to obtain the probability density function. Next, graph theory is used to establish the road network topology, introducing traffic saturation factors to adjust the EV’s driving speed. The Dijkstra algorithm is then applied to simulate the EV’s travel trajectory. Building on this, a fuzzy multi-attribute decision-making algorithm is employed to make decisions regarding the charging behavior during the travel process. Finally, a case study in a district of a southern Chinese city is conducted to simulate and predict the daily charging load distribution of electric private cars. The simulation results demonstrate that the proposed method can effectively predict the distribution patterns of EV charging loads across different time periods and functional zones, providing data support for charging station planning and EV load scheduling.