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Because of more and more flight delays due to the increasing air traffic demands, merely depending on a mono-objective programming to solve problems such as departure slot, flight route and air traffic control (ATC) workload cannot meet the need of the air traffic flow management. In this paper, a multi-objective and non-linear model is developed, comprehensively considering the problems above and picking up factors causing airspace congestion and flight delays. Then, a multi-objective genetic algorithm is designed to solve this model. The simulation results based on the operational flight data prove that the model and the algorithm cannot only optimize the departure time and route for each flight within reasonable time-horizon, but also reduce the ATC workload. It makes air traffic flow management coincident with the actual operation, and finally effectively reduces the airspace congestion.