This work presents an improved genetic algorithm (IGA) for minimizing periodic preventive maintenance costs in series-parallel systems. The intrinsic properties of a repairable system, including the structure of reliability block diagrams and component maintenance priorities are considered by the proposed IGA. The proposed component importance measure considers these properties, identifies key components, and determines their maintenance priorities. The optimal maintenance periods of these important components are then determined to minimize total maintenance cost given the allowable worst reliability of a repairable system. An adjustment mechanism is established to solve the problem of chromosomes falling into infeasible areas. An elitist conservation strategy is applied to retain superior chromosomes in the iterative breeding process and to accelerate the approach toward the global optimum. A response surface methodology is further used to systematically determine crossover probability and mutation probability in the GA instead of using the conventional trial-and-error process. A case study demonstrates the effectiveness and practicality of the proposed IGA for optimizing the periodic preventive maintenance model in series-parallel systems.