In this study we consider a Dynamic Genetic Algorithm used to optimize the movement of a symmetric six-legged creature. The optimal movement is that which advances the creature in a straight line forward with the greatest average speed. The mutation rate and crossover rate are adjusted based on number of iterations the algorithm has completed. This dynamic element was added to improve convergence rate as well as reducing the chance that the algorithm is stuck in a local optimum. The chromosomes are represented by a 2-dimensional array, where the rows represent sequences of movement. Each row defines the change in the angle for all the joints. Angular rates are restricted per joint, as well as ranges of motion. The fitness of a chromosome is determined by the resultant average speed, calculated as total displacement of the center of gravity over total time of movements in the chromosome. The results of this study show the possibility to breed mathematically the creature by using the Dynamic Genetic Algorithm proposed. This learning process converged, for all the simulations carried out, to the natural motion of six-legged beings like the ants.