The work aims to optimize air material inventory structure and solve the problem of multi-objective air meterial allocation which is helpful to improve the efficiency of air material support decision-making. A multi-objective air material allocation model was established based on cost and allocation satisfaction. The advanced intelligent algorithm-GOA was adopted to solve it. The example analysis showed that among the three algorithms, the results solved by grasshopper optimization algorithm not only minimized the cost in air material allocation, but also guaranteed high satisfaction of air material stocks. At the same time, the three algorithms were executed ten times. The mean and variance of grasshopper optimization algorithm were 4.01 ms and 11.5 ms. It was evidently superior to solution efficiency of the traditional group of particle swarm intelligence algorithm and the NSGA-Ⅱ algorithm. GOA can effectively solve the problem with the allocation of multi-objective air materials. It has practical significance for optimizing air material inventory and balancing air material quantity.