The energy collected from the thermoelectric generator; TEG is mainly dependent on the temperature difference between the hot and cold sides of the TEG along with the connected load. Therefore, a maximum power tracker is needed to force the TEG to work near the maximum power with any variation during operation. In the present work, an optimized fractional maximum power point tracker; OFMPPT is suggested to improve TEG performance. The suggested tracker is based on fractional control. The optimal parameters of OFMPPT were determined using the grey wolf enhancer; GWO. To demonstrate the superiority of GWO, the results were compared with the particle swarm optimization; PSO methodology and the genetic algorithm; GA. GWO achieves a largest fitness function, lowest standard deviation, and highest efficiency. The objective of the proposed OFMPPT is to overcome two major problems in conventional tracking devices. It is the slow dynamics of the conventional incremental resistance tracker; INRT as objective function and the high steady-state variability around maximum power point; MPP in the observation tracker and very common perturbation perturb & observe (POT). The main result confirmed the superiority of OFMPPT compared to INRT and POT for both