Finding the most appropriate path in robot navigation has been an interesting challenge in recent years. A number of different techniques have been proposed to address this problem. Heuristic methods are one of them that have been efficiently used in many complex and multi-dimensional optimization problems. In this paper, we present a new algorithm for robot path planning in a static environment. The main aim is to use a multi objective method to minimize several metrics such as cost, distance, energy or time. Distance, path smoothness and robot path planning time is optimized in the current work. The contribution of this work is to calculate an appropriate fitness function at each iteration to achieve the best solution. The obtained result is compared with the Particle Swarm Optimization (PSO) algorithm. The proposed algorithm displays better performance characteristics in terms of time and path smoothness than PSO algorithm and the obtained path lengths are shorter than those obtained with PSO.