At present, most studies determine EV charging needs based on user travel paths, but they have not taken multiple uncertainties into account. Based on user travel data, this paper considers various uncertain factors, simulates EV charging requirements in three scenarios, and uses a K-means clustering algorithm to obtain charging requirements in those scenarios. Secondly, based on charging requirements, a multi-objective planning model that comprehensively considers the interests of both the user side and the non-user side is established. In addition, this paper also proposes an improved queuing theory that considers multiple scenarios to fix the capacity of charging stations. The proposed planning model is solved by NSGA-II, and the TOPSIS comprehensive evaluation method is used to determine the planning scheme. Finally, taking the urban area of Weifang in Shandong Province as the planning area, and the transportation network coupled with the IEEE 69 node distribution network as an example, the effectiveness and superiority of the planning model are verified. This work is supported by the Youth Fund of National Natural Science Foundation of China (No. 51507052).