Task scheduling in distributed environments is an important research field. To address the issue of effective resource scheduling, a resource scheduling model using data gridding is proposed in this paper. First, we use data gridding on the attributes of resources to divide different types of resources into different data subspaces. We then search the gridding data for resources satisfying the minimum requirements of the task, which reduces the blindness of the allocation of resources and decreases the time needed for resource matching and scheduling. Finally, a globally optimized resource allocation strategy to minimize the task execution time is adopted to choose from among the resources satisfying the minimum task requirements. Results of experimental and theoretical analyses show that the proposed algorithm has a higher task-matching speed and shorter task-execution time than traditional algorithms.