Abstract: In edge command and control scenario, the mobile device scheduling problem in multi-task federated learning（FL）is modelled as a multi-objective optimization problem. Under the constraints of model convergence, transmission reliability, and limited computation resources, the optimization goal is to minimize the training delay and energy consumption of each round of multiple FL tasks. To solve the 0-1 integer programming problem, a mobile device scheduling algorithm based on differential evolution is proposed. The proposed algorithm treats each feasible scheduling policy as an individual, and obtains a sub-optimal solution with optimum fitness through crossover and mutation iterative evolution. Simulation results show that the proposed algorithm can ensure the accuracy of the model, and can effectively reduce the time and energy consumption in the training process.