AbstractMost existing pavement maintenance and rehabilitation (M&R) decision-making approaches ignore the length constraint of preventive maintenance (PM). This study proposed a practical optimization-based decision-making approach that incorporates the length constraint of PM. For the current popular bottom-up decision-making approach, incorporating the length constraint of PM will cause its solution methods (i.e., evolutionary algorithms) to fail. Therefore, this study developed a sliding-window random repair (SWRR) method based on repair methods and sliding-window methods. The SWRR method was inserted into the evolutionary algorithm (i.e., genetic algorithm) of a two-stage bottom-up approach to solve this dilemma. That is, the proposed decision-making approach is composed of the SWRR method and the two-stage bottom-up approach. A parametric study showed that the M&R decision-making plan recommended by the proposed approach was less expensive than the actual engineering plan, but the achieved performance was 5.7% higher. The results prove that the proposed approach can indeed solve the dilemma caused by the length constraint of PM and produce a better decision-making plan.