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With the proliferation of electric vehicles, the Electric Vehicle Charging Scheduling (EVCS) becomes a critical issue in the modern transportation systems. The EVCS problem in practice usually contains several important but conflicting objectives, such as minimizing the time cost, minimizing the charging expense, and maximizing the final state of charge. To solve the multiobjective EVCS (MOEVCS) problem, the weighted-sum approaches require expertise to predefine the weights, which is inconvenient. Meanwhile, traditional Pareto-based approaches require users to frequently select the result from a large set of trade-off solutions, which is sometimes difficult to make decisions. To address these issues, this paper proposes a Heterogeneous Multiobjective Differential Evolution (HMODE) with four heterogeneous sub-populations. Specially, one is for the multiobjective optimization and the other three are single …