Disaster relief demands urgent need for rapid transport of supplies. Vehicle transportation scheduling is based on large-scaled and real-time traffic network with time limit constraints and various supplies for multiple destinations lay the difficult points. To attain the target, multi objective optimization model for vehicle emergency transportation is built, and an optimized genetic algorithm is put forward to solve the vehicle scheduling problem in emergency rescue with multiple constranins including road variation, arrival deadlines and multiple materials. Based on the theory of biological evolution and principles of population genetics, the optimized algorithm takes the multi spots and in-fact road conditions into consideration, makes focus on the multiple material demands and the precedence on the arrival time without delay, designed a new coding pattern which made new encoding mode design, adaptive function, new selection, crossover and variation operators, and new generation mechanism to produce more and better patterns in less time, so as to overcome the premature convergence of the classic genetic algorithm. Tests proved the better performance of the proposed algorithm in finding the global optimized solution than the traditional genetic algorithm in delivery time constrains and the total length of arrival time, with less vehicles involved and less solving time. The proposed method can improve the transportation efficiency of disaster relief and cut down the vehicle cost, and meet the demands of complex vehicle scheduling tasks.