Evolutionary robots can achieve certain goals via evolutionary algorithms without specifying all the detailed actions. The robot interacts with the environment and receives natural feedback as the fitness of its goal. In this paper, we study two biomorphic robots and observe how the behavior can evolve with least computation resources and test the possibility of self-adjustment on an unexpected motor failure of one robot leg. The feedback is collected via a supersonic sensor and the evolutionary algorithm is a modified low cost genetic algorithm. In order to minimize the cost of evolution, we control the number of the population of each generation to a very low number. We find that the biomorphic robots can move faster with the motion sequence generated by the evolutionary algorithm than with the motion sequence assigned by human programmers. The structures of robots, in this case, and the number of legs might affect the speed of convergence. Our experiment result shows that the robot can adjust itself with this online evolving approach to deal with an unexpected motor fault. That is, evolutionary robot can overcome an unexpected situation without human intervention.