Aiming at the holiday of electric vehicle charging load and the uncertainty of load prediction,the article constructs an electric vehicle charging station planning based on the conditional value-at-risk. Firstly, based on the travel chain, combined with the Dijkstra shortest path algorithm, the Monte Carlo random simulation is used to obtain the spatiotemporal distribution of fast and slow loading. The location model is established by taking the minimum number of charging stations as the target and satisfying all the charging demand points. On this basis, the user"s waiting time is considered, the constant volume model is established with the goal of minimizing the construction cost of the charging station. In order to solve the charging load holiday and load prediction randomness in the capacity model, the constant volume model is transformed into the opportunity constrained programming by robust optimization, and the conditional risk value tool is introduced to solve. Finally, a regional charging station is used as a simulation example to discuss the sensitivity analysis of the model parameters. The numerical results show that the proposed method can enhance the robustness of the charging station planning model and is feasible.