Energy-aware resource management in cloud computing has attracted lots of attention and many approaches have been proposed. A commonly used technique is server consolidation, which consolidates virtual machines (VMs) on a fewer physical servers and switches idle servers to low-power modes. Nevertheless, the majority of the proposed approaches do not consider load balance of active servers, which is an important issue that should not be ignored. In this paper, we investigate the problem of energy-aware resource management in cloud computing by taking load balance into account and formulate this problem as a multi-objective optimization model. The first optimization objective is to minimize the number of active servers and the second one is to balance the loads among these servers. Based on the proposed optimization model, a heuristic-based algorithm called greedy-based load balance (GBLB) algorithm is developed. Since reducing the number of active servers generally increases the number of VM migrations, we further minimize the number of VM migrations in the proposed GBLB algorithm. Simulation results show that, compared with other three popular algorithms, the proposed GBLB algorithm can reduce the number of active servers and achieve the best load balancing level at the cost of a few more migrations.