Grid computing is characterized by the existence of a collection of heterogeneous geographically distributed resources that are connected over high speed networks. Job scheduling and resource management have been a great challenge to researchers in the area of grid computing. Very often, there are applications having a large number of fine-grained jobs. Sending these fine-grained jobs individually to be executed on grid resources that have high processing power reduces resource utilization and is thus uneconomical. This paper presents efficient grouping-based scheduling models that group fine-grained jobs to form coarse-grained jobs which are sent for execution on grid resources. Our grouping strategy is based on the processing capability of resources and the processing requirements of grouped jobs. A load balancing approach is also presented to achieve efficient utilization of resources. Simulation experiments were conducted using the Gridsim toolkit. Results show that the total simulation time and the cost are improved by grouping. Furthermore, our load balancing approach enhances resource utilization and achieves load balancing among resources.