This study focuses on the evaluation of different methods of product recovery for GM. The evaluation is conducted through the application of a decision making model. The model evaluates product recovery options on the basis of two categories: optimization of objective factors and market demand. The first category in the model focuses on optimization of five objective factors, including environmental impact (E), cost (C), quality (Q), resource consumption (R), and time (T). Goal programming is used to solve the optimization problem. The goal programming is supported by the construction of a decision making tree with three branches: remanufacturing, refurbishing, and current manufacturing. The solution of the decision tree helps determine the best method of product recovery for GM.The second category in the model focuses on the evaluation of market demand. This further supports the selection of the best method for product recovery. To evaluate market demand, a Bayesian forecasting model is used in the construction of a decision making tree. The study shows that the availably of products information including the objective factors and market demand, has a positive impact on making product recovery decisions. It also shows how recovery decisions can be modeled in decision making tress to represent the impact of product information on those decisions.