Aiming at the holiday of electric vehicle charging load and the uncertainty of load prediction, this paper constructs an electric vehicle charging station planning based on the CVaR (conditional-value-at-risk). Firstly, based on the travel chain, combined with the Dijkstra shortest path algorithm, the Monte Carlo random simulation is adopted 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 waiting time of users is considered, and 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-value-at-risk tool is introduced to solve. Finally, a regional charging station is utilized as a simulation example. The numerical results show that the proposed method can enhance the robustness of the charging station planning model and is feasible.