Combining the genetic algorithm and simulated annealing algorithm with the culture algorithm, we design a hybrid culture optimization algorithm to solve the traveling salesman problem (TSP). The strategy contains two parts: the population space and the reliability space. The population space evolves according to the hybrid genetic annealing algorithm and sends optimal individuals to the reliability space. The reliability space extracts the information contained by the optimal individuals to guide population evolution. Experiments on TSP benchmark show that compared with other optimization algorithms, the hybrid cultural optimization strategy can reduce the deviation rate of the result to be 0.6% to 13.01% when obtaining the optimal path. Experiments verify the effectiveness and superiority of the hybrid cultural optimization strategy for solving the TSP.