Concerning low solution accuracy and slow convergence of traditional Artificial Cooperative Search （ACS） algorithm， a Quasi opposition Artificial Cooperative Search algorithm based on Sigmoid function （SQACS） algorithm was proposed to solve Traveling Salesman Problem （TSP）. Firstly， the Sigmoid function was used to construct the scale factor to enhance the global search ability of the algorithm. Then， in the mutation stage， the mutation strategy DE/rand/1 of Differential Evolution （DE） algorithm was introduced into the current population for secondary mutation， thereby improving the calculation accuracy of the algorithm and the diversity of the population. Finally， in the later development stage， the quasi opposition learning strategy was introduced to further improve the quality of the solution. Four instances in TSP test library TSPLIB were used to perform simulation experiments， and the results show that SQACS algorithm is superior to seven comparison algorithms such as Sparrow Search Algorithm （SSA）， DE and Archimedes Optimization Algorithm （AOA） in the shortest path and time consumption， and has good robustness； and compared with other improved algorithms for solving TSP comprehensively， SQACS algorithm also shows good performance. Experimental results prove that the SQACS algorithm is effective in solving small-scale TSPs.