An evolutionary multi-objective optimization method is proposed for the traveling salesman problem(TSP) to overcome the parameter-setting trouble in traditional niching strategies, where we develop an optimization model minimizing the tour length as well as the averaged mutual distance, and solve it to get the global optimal solutions of TSPs via the nondominated sorting genetic algorithm II(NSGAII).To strike a balance between global exploration and local exploitation, it incorporates an evaluation strategy that makes different solutions of the same length nondominated, and generates new candidate solutions via a novel discrete differential evolution strategy as well as a simplified 2-Opt strategy.Numerical comparisons with existing algorithms demonstrate that the NSGAII for TSP(NSGAII-TSP) can better keep the population diversity, be kept away from the absorption of local optima, and consequently has robust global exploration ability.With assistance of the special strategy making different solutions of the same length nondominated, the proposed algorithm can perform global optimization better, and locate several global optimal solutions at a time.