Flight time optimization is an important means to solve the balance of airport capacity and flow, and has always been the focus of research in the field of air traffic. In view of the fact that the existing research mainly focuses on the fairness of time allocation for inbound and outbound flights, but there are few studies that comprehensively consider the influence factors of flight revenue and controller load, a multi-objective flight time optimization model is proposed to maximize the revenue of airline operation, minimize the workload of controllers, and minimize the deviation of fairness, and the improved genetic algorithm is used to solve the model, which is validated by the actual flight data of Shenzhen Bao'an International Airport. The effectiveness of the model is verified with the actual flight data of Shenzhen Baoan International Airport. The results show that after optimization, the average controller workload fairness deviation coefficient is decreased by 7.97%, the average airport fairness deviation is decreased by 15.30%, and the overall average airline revenue is increased by 0.21%, which achieves the purpose of improving airline revenue and reducing controller load.