Correlating a thermal model could be a challenging problem in global optimization, which often requires employing a genetic algorithm. This work studies choosing the optimal set of the genetic algorithm control parameters when it is used for this purpose. Two practical thermal models are considered. For the small model the optimal set of the genetic algorithm parameters was found by applying a double genetic algorithm where the outer algorithm tried to minimize the best fitness value achieved by the inner one by varying the inner algorithm control parameters. In addition, it was demonstrated that in a practical calculation increasing the population size can easily result in a worse accuracy. The proper choice of the population size was studied for the large model. The double genetic algorithm was modified by starting each run of the inner algorithm with the best solution obtained until then, instead of using the same initial population in all the runs. Doing all the runs with the same population size and choosing the population size randomly were also tried and it was shown that this way a considerable improvement in accuracy could be achieved.