With the rapid development of high-speed wide-area networks and powerful yet low-cost computational resources, grid computing has emerged as an attractive computing paradigm. The space limitations of conventional distributed systems can thus be overcome, to fully exploit the resources of under-utilised computing resources in every region around the world for distributed jobs. Workload and resource management are key grid services at the service level of grid software infrastructure, where issues of load balancing represent a common concern for most grid infrastructure developers Several decentralized load balancing policies have been proposed to address the issue of scalability in grids. Managing resources and cleverly pricing them on computing systems is a challenging task. Resource sharing demands careful load balancing and often strives to achieve a win-win situation between resource providers and users. Toward this goal, we consider a joint treatment of load balancing and pricing. We do not assume static pricing to determine load balancing, or vice versa. Instead, we study the relationship between the price that a computing node is charged and the load and revenue that it receives. We find that there exists an optimal price which maximizes the revenue. We then consider a multi-user environment and explore how the load from a user can be balanced on processors with existing loads. Finally, we derive an optimal price that maximizes the revenue in the multi-user environment. We evaluate the performance of the proposed algorithms through simulations.