Scientific computing centers or private (in-house) cloud data centers do not rely on the standard pay-as-you-go business model which is common in commercial clouds to allocate resources. Instead, the system is typically shared by a set of selected users, and the administrator’s job is to ensure that resources are shared fairly given the existing policies of that organization. One common approach, especially in batch systems, is to deploy a fairshare-based prioritization in the scheduler, where a prioritization mechanism balances resource consumption so that individual users get the right shares of resources over time. In this paper, we present a tool developed to simulate the fairshare setting in a batch system. Using a set of experiments, we demonstrate the utility of this tool in tuning fairshare settings in a standard HPC/HTC scheduler and present the impact of often-overlooked additional options for modifying the basic fairshare settings. All the findings in this paper are based on our real-world experience of running and optimizing a distributed national computing infrastructure in the Czech Republic.