Pain is the most common symptom when a patient visits a physician. People experience pain throughout their lifetime at different degrees. If short term pain is not treated properly, then it can become long term pain, which is also known as chronic pain. The Eugene McDermott Center for pain management at UT Southwestern Medical Center conducts a two-stage pain management program for chronic pain. This research uses a two-stage stochastic programming approach to optimize personal adaptive treatment strategies for pain management. The goal is to generate adaptive treatment strategies using statistics based optimization approaches that can be used by physicians to prescribe treatment to the patients. Transition models predict how a patient with certain characteristics will react to treatments. This research uses Piecewise Linear Networks (PLN) to represent transition models. A mixed integer linear program is developed to integrate those PLN transition models into an optimization problem.