Roads are considered as one of the most extensive civil infrastructures and national capitals of each country. Pavement maintenance and rehabilitation (M&R) management is one of the most important problems, which can help reducing further costs. Two issues are important in the field of road maintenance and rehabilitation management; first type of M&R option and then time of applying it. In this paper, we have attempted to combine prioritization, artificial intelligence and optimization methods to select the optimal option for maintenance and rehabilitation of pavement sections at any time interval. For this purpose, the analytical hierarchy process is used to prioritize the branches of the pavement network. In the next step, using the linear programming model, the probability of selecting maintenance and rehabilitation options for pavement sections is maximized considering several specific constraints. Fuzzy inference system is used to determine the probability of selecting each maintenance and rehabilitation option in pavement sections. A case study in Mahan (Kerman) is used to run the proposed model. Based on the results, it can be concluded that the proposed algorithm can consider different parameters and indices for pavement branches and sections. In addition, it offers different scenarios for selection of M&R options in a year. The model helps comparing various scenarios based on different budgets for each year. In all, the proposed algorithm facilitates the process of selecting M&R options in the different sections of a road network and provides a scientific approach to manage maintenance roads.