Cloud manufacturing resources have high demands for scheduling and scheduling granularity because of its dispersion, diversity and load ration imbalances, etc. In this paper, a new multi-objective model for manufacturing resources process scheduling is proposed. The model decomposes the manufacturing task into process as scheduling minimum granularity to minimize the total time of manufacturing service, the total cost of manufacturing services and the balanced load rate. The new model adopts hybrid algorithm which combines genetic algorithm(GA) and particle swarm optimization (PSO), uses the chromosomes of GA as particles in the hybrid algorithm, and carries out the double-layer coding by using the processing order as the first layer and the corresponding processing resource number as the second layer. Then particles are by the updated crossover and mutation of chromosomes to achieve a faster and more accurate optional solution for the algorithm converges. At last, the example of elevator proves that the new model can get optional scheduling scheme in a short time and effectively solve the multi-objective scheduling problem for cloud manufacturing resource.