This thesis presents an optimization model which helps retailers to reduce product costs by taking advantage of parts commonality in manufacturing and production areas, when selling similar units with uncertainty in demands. The concept of component commonality can be often found in the assemble-to-order system, which is the foremost concept used by prominent manufacturing companies in the global market. The method developed uses genetic algorithm (GA) to solve real world optimization problems that contain integer values for parts and finished items, and uncertain information. Numerical examples are solved using generated stochastic scenarios to show the impact of uncertainty on solutions. This impact is verified using two important criteria, Expected Value of Perfect Information (EVPI) and Value of Stochastic Solution (VSS). The obtained solutions present significant monetary benefits for the manufacturer illustrating the importance of the model presented here for retailers.