This work presents a general vision about the main concepts of \ncombinatorial multi-objective optimization, where we present the more used \ntechnique for the resolution of problems of this nature. To the speech of the \ntechniques we will also argue important aspects how much to the involved \nparameters in each technique, swing the main used boardings. Initially we \nimplement and test the Multiple Objective Genetic Algorithm MOGA to \ngenerate a set of dominant solutions near to the Pareto optimal set for the biobjective \nTraveling Salesman Problems. In a second phase, we will go to \nimplement the Strength Pareto Evolutionary Algorithm (SPEA) applied to biobjective \nTraveling Salesman Problems