When emergencies occur in cities, efficient emergency material distribution capabilities are one of the effective ways to reduce losses of life and property. To improve the efficiency and effectiveness of urban emergency supply distribution, a multi-UAV cooperative path planning algorithm based on evolutionary multi-task optimization (MCPP-EMTO) is proposed. In this algorithm, the original multi-UAV emergency distribution problem is regarded as the main task, while the multi-UAV emergency distribution problem disregarding endurance and capacity constraints of UAVs is regarded as an auxiliary task. Moreover, the proposed algorithm transfers the useful evolutionary information from the auxiliary task to the main task to improve the solution efficiency. In order to verify the performance of the proposed algorithm, three different urban emergency distribution scenarios are used, and four high-performance multi-objective evolutionary algorithms are selected as comparison algorithms. Simulation experiments show that compared with the four comparison algorithms, the proposed algorithm can obtain Pareto fronts with better diversity and approximation under different scenarios.