Abstract: Aiming at the coordinated charging problem of large-scale electric vehicles（EVs）within a large charging station, an EV charging scheduling strategy based on double deep Q network（DDQN）of deep reinforcement learning is proposed, which can effectively take into account the uncertainty of traveling pattern and charging demand of EVs and achieve the objective of minimizing the charging cost of the charging station. Firstly, characteristics of the parking time and the charging demand of EVs are extracted. Secondly, to solve the curse of dimensionality problem, a binning method and an optimal charging order strategy are proposed to control the size of the state and action space, and thus a Markov decision process（MDP）model which is applicable to the coordinated charging of large-scale EVs is established. Then, the DDQN of reinforcement learning algorithm is used to calculate the EV coordinated charging strategy. Finally, the validity of the proposed method is verified through a simulation example. It is verified that not only the charging cost of the charging station can be effectively reduced, but also the difficulty of model training becomes unaffected by the scale of EVs.