With the development of the civil aviation transportation industry， the density of flights is increasing， and the shortage of near-airport resources in large airports has become increasingly prominent. The lack of near-airport resources reduces the efficiency of flight guarantees. In order to develop a comprehensive and effective solution to the problem of gate pre-allocation in large hub airports， based on the actual situation of airlines， airports and passengers， a multi-objective parking space allocation optimization model is established， and an improved non-dominated sorting genetic algorithm（NSGA-Ⅱ） with an elite strategy is designed to solve the model. The Pareto frontier solution is obtained. In the crossover and mutation operation stage， the crossover rate and the mutation rate are adaptively adjusted for the population individuals in an exponential form， so as to improve the convergence speed of the algorithm and the diversity of excellent solutions. The example verification results show that the optimization results of the model and the improved algorithm are more prominent than the manual allocation and the traditional NSGA-Ⅱ algorithm for parking space assignment， especially in terms of the bridge rate and the number of used parking stands. At the same time， the performance evaluation index is used to compare the two algorithms， and found that the improved NSGA-Ⅱ algorithm is more suitable for solving the gate pre-allocation problem.