Multi-traveling salesman problem is an evolution of the classic traveler problem. Considering some constraints, it can be converted into some more realistic problems,with high theoretical research and application value. In the multi-traveling salesman problem,a task is completed by a number of travel agents,the problem is more difficult than the classic traveler problem. In the existing study,they convert the problem into a fixed number of traveling salesmen problem. In this paper,we construct a multi-objective multi-traveling salesman problem model for seeking the pareto solu-tion. In view of the problem of the number of cities and the constraints of a certain scale,we obtain the number of trave-ling salesmen of the problem. In this paper,the number of traveler and the longest access path of multi-traveler are taken as the optimization target. The improved multi-objective simulated annealing(IMOSA)algorithm and multi-objective genetic algorithm are used to solve the problem. The results show that the improved multi-objective simulated annealing algorithm is more complex than the multi-objective genetic algorithm and can find a better pareto solution and the algo-rithm performance is better than that of the multi-objective genetic algorithm.