We apply a treatment simulation and optimization approach to develop decision support guidance for warfarin precision treatment plans. Simulation include the use of ∼1,500,000 clinical avatars (simulated patients) generated by an integrated data-driven and domain-knowledge based Bayesian Network Modeling approach. Subsequently, we simulate 30-day individual patient response to warfarin treatment of five clinical and genetic treatment plans followed by both individual and subpopulation based optimization. Sub-population optimization (compared to individual optimization) provides a cost effective and realistic means of implementation of a precision-driven treatment plan in practical settings. In this project, we use the property of minimal entropy to minimize overall adverse risks for the largest possible patient sub-populations and we temper the results by considering both transparency and ease of implementation. Finally, we discuss the improved outcome of the precision treatment plan based on the sub-population optimized decision support rules.