Abstract. Autonomous mobile robots have been achieving significant improvement in recent years. Intelligent mobile robots may detect hazardous materials or survivors after a disaster. Mobile robots usually carry limited energy (mostly rechargeable batteries) so energy conservation is crucial. In a mobile robot, the processor and the motors are two major energy consumers. While a robot is moving, it has to detect an obstacle before a collision. This results in a real-time constraint: the processor has to distinguish an obstacle within the traveled time interval. This constraint requires that the processor run at a high frequency. Alternatively, the robot's motors can slow down to enlarge the time interval. This paper presents a new approach to simultaneously adjust the processor's frequency and the motors' speed to conserve energy and meet the real-time constraint. We formulate the problem as non-linear optimization and solve the problem using a genetic algorithm for both continuous and discrete cost functions. Our experiments demonstrate that more energy can be saved by adjusting both the frequency and the speed simultaneously.