Abstract: The optimal allocation of cloud manufacturing resources currently faces issues such as low service matching efficiency and resource utilization rate. To overcome the limitations of the spatio-temporal distribution of manufacturing resources and achieve cross-enterprise collaborative sharing, a resource optimization allocation model based on six-dimensional indicators, namely time, cost, quality, service, flexibility and credibility. To improve the efficiency of model solution, a hybrid optimization method integrating the simulated annealing algorithm and the adaptive genetic algorithm has been proposed to enhance the global search ability and accelerate the convergence speed. The manufacturing task of planetary gear reducers was used as a example for verification. The experimental results show that the proposed algorithm is superior to traditional algorithms in terms of fitness value, iteration efficiency and running time. The optimized resource allocation scheme can significantly improve the system response ability and resource utilization level, verifying the feasibility and superiority of this method.