Knowledge about the workload is an important aspect for scheduling of resources as parallel computers or grid components. As the scheduling quality highly depends on the characteristics of the workload running on such resources, a representative workload model is significant for performance evaluation. Previous approaches on workload modelling mainly focused on methods that use statistical distributions to fit the overall workload characteristics. Therefore, the individual association and correlation to users or groups are usually lost. However, job scheduling for single parallel installations as well as for grid systems started to focus more on the quality of service for specific-user groups. Here, detailed knowledge of the individual user characteristic and preference is necessary for developing appropriate scheduling strategies. In the absence of a large information base of actual workloads, the adequate modelling of submission behaviors is sought. In this paper, we propose a new workload model, called MUGM (mixed user group model), which maintains the characteristics of individual user groups. The MUGM method has been further evaluated by simulations and shown to yield good results.