The primary goal of pavement maintenance and rehabilitation (M&R) planning is to achieve transportation agencies’ objectives within limited budgets. Therefore, numerous optimization models have been developed for scheduling M&R activities at project or network levels; however, their effectiveness is contingent upon pavement condition deterioration and improvement, M&R alternatives, and various climatic, environmental, and regional factors. Moreover, these models should produce comprehensive, efficient, and practical schedules on which highway agencies can rely. This research study introduces a framework and optimization model for large road networks with different types of pavement surfaces, considering various pavement condition indicators and distresses. Employing the genetic algorithm (GA), a single-objective optimization model was created to generate optimal M&R schedules. The reliability of this GA model was tested to ensure the generation of optimal solutions. Furthermore, the model’s sensitivity to budget variations was assessed. The developed framework and GA model were used to analyze Interstate 80 sections in Wyoming, USA. The results demonstrate that the GA model consistently achieved the most cost-effective outcomes over a 5-year period, with fewer than 1,000 iterations. Moreover, given the available budget, implementing the generated treatment plan has the potential to preserve road networks in good condition. The findings suggest that optimization models should consider multiple condition indicators and distresses when developing practical schedules for effective pavement maintenance. It was unearthed that funds should be allocated among network components based on their relative conditions. This article could assist policymakers and asset managers in making budgetary decisions and developing M&R strategies for the long-term sustainability of transportation networks.