Abstract: The existing cloud workflow scheduling algorithms, using the global search for resource selection, exist a high computational cost and poor adaptability for large-scale cloud systems. Aimed at solving these problem, a multi-constrained cloud workflow scheduling algorithm based on resource grouping is proposed in this paper. It uses the direct acyclic graph to model the multi-task in cloud workflow and characterize the execution sequences and data transfer requirement between tasks with the DAG's node and edge's attributes. Then, fuzzy clustering method is employed to classify resources based on multidimensional features and reduce the computational load from workflow tasks to resource selection. By introducing execution time and cost budget constraints, the proposed algorithm transforms the scheduling problem into a minimax problem. Simulation results show that our algorithm significantly reduces the task execution time and cost.