Current multi-objective genetic algorithms usually can only attain part of the whole pareto front, at the same time, because of the worse local searching ability, the convergence speed is slow. In order to overcome these disadvantages, an updated multi-objective genetic algorithm is proposed in this paper. The updated algorithm not only integrates the merits of the Non-dominated Sorting Genetic Algorithm (NSGA) and the Vector Evaluated Genetic Algorithm (VEGA), but also has a local searching operator which constructs the searching direction by using the previous population's information, so it can effectively expand the scope of non-inferior solutions and improve the convergence speed. Using the updated algorithm, this paper succeeds in optimizing a large unmanned aircraft wing structure. The result indicates that the new algorithm can rapidly acquire uniform non-inferior solutions and prove the superiority of the algorithm.