Traortation planning is a process which determines necessary actions according to achieve the desired state of a traortation system that is determined by traortation’s experts. One of the most important issues in this field is the selection and scheduling of traortation investment projects.This problem has some properties such as multiple stakeholders and decision makers, a variety of evaluation criteria, existence of numerous alternative interdependencies between projects, and uncertainty of information that should be considered. Therefore, a portfolio of proposed projects should be selected in a way that satisfies resources constraints and achieves the maximum benefits. Hence, if the selection and scheduling of traortation investment projects do not perform with a systematic view, not only the system situation does not improve but there is a possibility of getting worse. In this field, most researchers have studied just projects selection and rarely integrating selection or scheduling of investment projects have been considered. In this research, model of integrated selection and scheduling of traortation investment projects is presented. A new measure to evaluate projects according to the threshold of user satisfaction is developed wich is combination with the total travel time form the objective function . Due to the complexity of computing the objective function and Braess phenomenon, providing common accurate methods for solving is very complex and difficult. Hence, the genetic algorithm is used as a metaheuristic algorithm in order to solve this problem. Also, its parameters are adjusted using Taguchi method. The MATLAB software is applied to implement the mentioned algorithm and to calculate the objective function of generated wich previously achieved through the GAUSS software. After solving the model for different dimensions of the problems, the results were analyzed and the performance of the proposed method is evaluated through implementation on a medium-sized network traortation (Sioux falls urban Network). The results have shown that the Genetic Algorithm is an efficient algoritm for solving the mentioned problem.