A key challenge in MapReduce environments is to increase the utilization of MapReduce clusters to minimize their cost. For a set of production jobs that are executed periodically on new data, we can perform an off-line analysis for evaluating performance benefits of different optimization techniques. In this work, we consider a subset of production workloads that consists of MapReduce jobs with no dependencies. We observe that the order in which these jobs are executed can have a significant impact on their overall completion time and the cluster resource utilization. We evaluate the performance benefits of the constructed schedule through an extensive set of simulations over a variety of realistic workloads. The results are workload and cluster-size dependent, but it is typical to achieve up to 10%-25% of makespan improvements by simply processing the jobs in the right order. However, in some cases, the simplified abstraction assumed by Johnson's algorithm may lead to a suboptimal job schedule. We design a novel heuristic, called BalancedPools, that significantly improves Johnson's schedule results (up to 15%-38%), exactly in the situations when it produces suboptimal makespan. Overall, we observe up to 50% in the makespan improvements with the new BalancedPools algorithm.