For the multi-objective resource allocation problem(MORAP), the traditional algorithms execute slowly and non-dominated solutions can’t uniformly distribute in the Pareto front. Therefore, a new memetic algorithm(HTGSA) is proposed. The simulated annealing algorithm is introduced to GA to strengthen the local search and convergence speed. To make the non-dominated solutions uniformly distribute in the Pareto front, a tabu constraint strategy is introduced to the survival selection process of genetic algorithm. The tabu constraint strategy can strengthen the diversity of the solutions and prevent non-dominated solutions over concentration in the Pareto front. Finally, the proposed algorithm is tested with the numerical simulation experiment comparing the results with ACO, GA and PSO. Experimental results on MORAP show that this hybridization can significantly accelerate the convergence speed and reduce the computation time.