In order to address the stochastic optimization scheduling problems brought by the high penetration of wind power and electric vehicles connected into active distribution network, this paper adopts a stochastic power flow method based on unscented transform to deal with uncertainties of wind power output, the charging pattern of electric vehicles, and the load of power grid. On this basis, this paper proposes a novel multi-objective stochastic optimization model of active distribution networks that aims to minimize the operational costs, the power losses, and load variance though controlling the charging power of electric vehicles and the output power of distributed generation. In addition, the multi-objective particle swarm algorithm is used to solve the proposed model. The proposed model is examined on transformed IEEE 33-bus test system. The simulation results show that optimal dispatching models that consider uncertainty and coordinated charging of electric vehicles can effectively cut down the operation cost of distribution networks, reduce the power losses, and decrease the difference between peak and valley, thereby verifying the validity and effectiveness of the proposed model. This work is supported by National Key Research and Development Program of China (No. 2016YFB0900100).