This study shows the importance of genetic algorithms in the application of computational problems extremely difficult to resolve due to an impractically large number of solutions. The genetic algorithms - GA are based on nature to generate optimal solutions to difficult problems to be solved computationally in which a population of individuals is created and submitted to genetic operators: selection, crossover and mutation in order to generate a process similar to the evolution these natural reaching a satisfactory solution of the problem in question. An extremely interesting and complex problem is the cleavage of proteins, which either is to find rules that involve combinations of amino acid sequences of various proteins. This is a problem with many solutions, because the number of combinations position / amino acid is proportional to the factorial of the number of positions and amino acids. Following the guidelines of the theory of evolution is a family of algorithms used to solve problems. The structures are organized following an abstract model of data and the test is done with a sequence fictitious.