Given the various problems of the cultural algorithm, such as slow convergence speed, high likeliness to fall into local optimum, and low population diversity, this study optimizes the design of the cultural algorithm and proposes a hybrid optimization algorithm that incorporates a genetic algorithm (GA) with an elite retention strategy and a simulated annealing (SA) algorithm into the framework of the cultural algorithm (CA). In light of the idea of co-evolution, this algorithm is divided into a lower population space and an upper belief space that share the same evolutionary mechanism but use different parameters. On the basis of the CA, a GA with an elite retention strategy is added so that the outstanding individuals in the population can directly enter the next generation to improve the convergence speed. An SA algorithm is added as its mutation characteristics can be leveraged to enable the algorithm to probabilistically jump out of the local optimum and accept inferior solutions and thereby increase population diversity. The function optimization results prove the effectiveness of the proposed algorithm. This algorithm is applied to solve the flow shop scheduling problem of minimizing the maximum completion time. The simulation results show that the proposed algorithm is superior to several other representative algorithms in convergence speed and accuracy.