One of the most practical optimization problems in the reliability field is the redundancy allocation problem (RAP). This problem optimizes the reliability of a system by adding redundant components to subsystems under some constraints. In recent years, various meta-heuristic algorithms applied to find a local or global optimum solution for RAP in which redundancy strategies are chosen. Among these algorithms, simulated annealing algorithm (SA) is a capable one and makes use of a mathematical analogue to the physical annealing process to finding the global optimum. In this paper, we present a new simulated annealing algorithm named knowledge-based simulated annealing (KBSA) to solve RAP for the series-parallel system when the redundancy strategy can be chosen for individual subsystems. In the KBSA algorithm, the SA part searches the solution space to find good solutions and knowledge model saves the knowledge of good solution and feed it back to the algorithm. In this paper, this approach achieves the optimal result for some instances in the literature. In order to evaluate the performance of the proposed algorithm, it is compared with well-known algorithms in the literature for different test problems. Finally, the results illustrate that the proposed algorithm has a good proficiency in obtaining desired results.