Coal preparation plant simulators are used to study flowsheet alternatives and determine product quality and quantity that can be produced under various operating conditions for individual circuits. Some simulators also provide an estimate of the costper ton of clean coal by determining the number and size of different processing units required for a specific raw coal tonnage and by computing both capital and operating costs for individual units. However, to the authors' knowledge, none of these simulators have the ability to optimize plant performance while satisfying multiple product quality constraints and selecting the most profitable flowsheet from many alternatives. A novel coal preparation plant simulator (SIU-Sim) that maximizes the overall plant yield using a robust optimization tool and selects the best plant cleaning circuit configuration based on maximum plant profitability is presented. The simulator also identifies the best operating conditions for individual circuits of a plant to satisfy multiple quality constraints for the overall clean coal product. It uses a genetic algorithms-(GA) based technique to maximize the overall plant yield for each cleaning flowsheet alternative and uses a net present value (NPV) analysis technique to maximize plant profitability for the entire life of a new plant or the remaining life of an existing plant. The step-by-step development of the simulator along with a case study demonstrating its specific features is presented.