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The traditional ant colony algorithm has the advantages of good convergence and robustness, but has a long convergence time in solving the problem of TSP. In order to improve the quality of the solution of the traditional ant colony algorithm, G-SAACO is proposed,the genetic algorithm and simulated annealing algorithm are introduced into the ant colony algorithm.. The idea of the algorithm was to introduce the variation and crossover strategy of genetic algorithm into the traditional ant colony algorithm to get the candidate solutions, which increased the diversity of the solution. And introduction of simulated annealing algorithm mechanism made the algorithm have higher probability of selecting poor solutions in a candidate set into the latest set. Besides,In the mechanism of controlling the temperature, the quality of the solutions was improved through backfire strategy. In order to test the improved ant colony algorithm, randomly selected parts of the city of the TSPLIB to simulate.Compared with the standard GA,simulated annealing algorithm and the traditional ant colony algorithm for traveling salesman problem(TSP).The results show that the algorithm has a strong ability to find good solutions, and the stability of the average value is enhanced.