With the rapid development of Internet technology, the information data generated by all industries and professions is growing at an exponential rate. The traditional vehicle scheduling algorithm in dealing with dynamic vehicle scheduling problem, already cannot satisfy real-time and large-scale scenario, while big data in Hadoop technology can be a good solution. Therefore, this study constructs a dynamic vehicle scheduling parallel intelligent optimization algorithm based on Hadoop. Based on traditional genetic algorithm, the Hadoop platform parallel computing mechanism is used to improve the weak global optimization ability and converging to local optimal solution of the algorithm. The improved algorithm can effectively cope with massive and rapid response of the vehicle scheduling. The result of numerical calculation shows that the algorithm of vehicle scheduling based on Hadoop can effectively improve the optimization performance of traditional scheduling algorithm and has a good acceleration ratio when dealing with large-scale vehicle scheduling problems.