Genetic algorithms are search and optimization techniques which have their origin and inspiration in the world of biology. They provide very good results in different kind of problems, but they are not free of problems. One of the most common problems that may arise with these techniques is that, despite a few generations obtain an approximation to the solution of the problem, they need considerably more to adjust to the final solution. To solve this problem Nature gives us, another time, a valid option. Fine Tuning techniques can model this transmission of knowledge between generations making slight variations in offspring before inserting it into the next generation. For its implementation, a new individual is generated from a solution (non best), changing slightly their genes. It can be performed by means a new Genetic Algorithm, with a lower number of individuals and its own configuration. On this way, solutions avoid local minima and introduce more variability in the global population that increase the possibilities to achieve the best solution. The developed solution uses this approach within a generic tool that makes possible that the user provides their own fitness functions to add any kind of problems. The software will allow to parametrize the execution and will show several graphics to control the evolution. Furthermore, to minimize the time for obtaining solutions the assessment of individuals is made under a distributed scheme. The control of the implementation of Genetic Algorithm will be made from a master computer, which delegated to other slave devices for evaluation and, if necessary, apply fine tuning.