To realize a plan for Electric Vehicle (EV) charging networks with a background of distributed photovoltaics, a chance constrained optimization is developed, in which stochastic characters from EV charging loads and distributed photovoltaic output are considered simultaneously. First, a scenario-based probability method is used to investigate the probabilistic power flow of distribution systems during a typical planning day. On this basis, the chance constraints on the voltage deviations and power flow over limits in the optimization are established. Given the total number of EV charging stations and the minimum construction capacity, the optimization minimizes the network loss of the distribution system by optimizing the construction location and capacity of the EV charging stations. A Genetic Algorithm (GA) is used to establish the optimization. In addition, to enhance the performance of the GA, the mutation operator and the crossover operator in the GA are customized according to the physical characteristics of the chance constrained optimization. A simulation based on IEEE 33-bus distribution systems validates the formulation and method proposed in this paper. This work is supported by the National Natural Science Foundation of China (No. 51877112), the Natural Science Key Research Program of Jiangsu Colleges (No. 18KJA470003) and the Basic Sciences Research Project of Nantong City (No. JC2019127).