A wireless sensor network (WSN) is a dedicated wireless network designed to gather and transmit data from numerous compact sensor nodes dispersed across a defined geographical area. These sensor nodes are equipped with sensors, processing capabilities, and wireless communication abilities, working in concert to monitor and collect data from the surrounding physical environment. The practical implications of this research reverberate within the realm of WSN development, encompassing the exploration of energy-efficient protocols and strategies tailored to diverse real-world applications, ranging from commercial to agricultural contexts. Of paramount importance in WSN is the capability for precise location identification. This sought-after feature indicates the exigency for addressing multifaceted challenges linked to resource scheduling and the tracking of moving objects within the network's purview. The intrinsic energy limitations of individual nodes perpetuate the discontinuity and sparsity inherent in sensor data, accentuating the intricacy of network operations. The endeavor to identify and track objects continuously necessitates a strategic approach. The proposed method leverages genetic algorithm (GA) to craft a fitness function. This function encompasses the refinement of network energy residue, estimation of distances, and the scope of connection coverage. By embracing this methodology, energy conservation gains traction, leading to a pronounced augmentation in the lifespan of the WSN. The practical manifestation simulations were conducted using Spyder (Python 3.11). Notably, these results exhibit a remarkable 92% improvement in energy reduction when contrasted with alternative algorithms. This augmentation not only bolsters node location accuracy but also extends the network's temporal longevity. Moreover, the experimental outcomes underscore the error of unknown nodes, substantiating its proficiency in minimizing localization discrepancies. This research embarks on the intricate trajectory of WSN optimization. By harnessing the capabilities of GA, it navigates the terrain of energy consumption optimization, longevity extension, and accuracy enhancement. The consequential simulations affirm the potency of the proposed approach, paving the way for more refined and efficient WSN operations. The GA approach greatly improves localization accuracy, expedites tracking of unidentified nodes, ensures efficient anchor node support, and reduces energy consumption by 92% compared to other algorithms, all while maintaining high accuracy and minimal location errors in WSN.