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In this work we are presenting the implementation part of our research which explores two of the main Evolutionary Computation techniques which are; Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO) to optimize the energy dissipation in a dynamic Wireless Sensor Network (WSN). We are evolving a hybrid algorithm by applying the GAs in the first phase to divide the sensor network into K-clusters (K-unknown). The output of the first phase will be used as an initial population for the particles in the Swarm which represents the dynamic Sensor Network. GAs proved to be used effectively in the optimization of static Sensor Networks, but for dynamic networks, PSO algorithms are more suitable since the swarms are moving objects by nature. Hence, in this work PSO algorithms are proposed to keep the optimum distances between the sensor nodes during the sensors movement.