Ant colony optimization (ACO) is an optimization computation inspired by the study of the ant colonies? behavior. The combinational optimization process sometimes is based on the pheromone model and solution construction process. It remains a computational bottleneck because the ACO algorithm costs too much time to find an optimal solution for large-scale optimization problems. In this paper, a quickly convergent method of the ACO algorithm with evolutionary operator (ACOEO) is presented. In the method, crossover and mutation operator together provide a search capability that enhance rate of convergence. In addition, we adopt a dynamic selection means based on the fitness of each ant. The tours of better ants have high opportunity to obtain pheromone updating. Finally, our research clearly shows that ACOEO has the property of effectively guiding the search towards promising regions in the search space. The computer simulations demonstrate that the convergence speed and optimization performance are better than the ACO algorithm.