A standard traveling salesman problem(TSP) under dual-objective strategy constrained is proposed in this paper, characterized by the fact that the demand of both as many as possible the numbers of nodes be visited in time and minimum trajectory distance. The motivation for this TSP problem under dual-objective strategy constrain stems from the coverage repair strategies for wireless sensor networks using mobile actor based on energy analysis, wherein a mobile robot replenishes sensors energy when it reaches the sensor node location. The Evolutionary Algorithm (EA) meta-heuristic elegantly solves this problem by the reasonable designed operators of crossover, mutation and local search strategy,which can accelerate convergence of the optimal solution. The global convergence of the proposed algorithm is proved, and the simulation results show the effectiveness of the proposed algorithm
