This study investigates the simulation, optimization and development of a solar tracking system aimed at enhancing photovoltaic solar energy capture. The optimization process is analysed from two perspectives: the control system and the mechanical system. For the control system, a methodology is proposed to identify the optimal control strategy for a two-axis solar tracking system, specifically applied to a stand-alone low power photovoltaic application. Initially, four control strategies (adaptive neuro-fuzzy inference system, artificial neural network, perturb & observe, and fuzzy logic controller) are selected based on the most commonly used strategies, and are subsequently simulated within the MATLAB environment. A comparative analysis is conducted to determine the best suited control strategy for the designed solar tracking system. The optimization of the mechanical system involves a through review of existing knowledge to uncover trends and challenges, which subsequently informs the analysis of motion range and load variations. This process establishes critical specifications, such as stroke length and load capacity, for commercially available electric actuators. Additionally, a structural analysis of the mechanical structure is conducted to guarantee resilience against wind forces, which aids in identifying optimal design parameters for enhanced system performance. Moreover, a cost analysis is undertaken to assess the financial feasibility of the proposed tracking system, addressing the need for economic justification and competitiveness within the energy market. This study presents the application of perturb and observe control algorithm, recognized as the most suitable technique, for a low-cost stand-alone dual axis solar tracking system. To validate the design, the elevation and azimuth angles obtained from the solar tracker are compared with those calculated using Joseph Michalsky’s solar position algorithm. The findings affirm the effectiveness of the proposed methodology and system design, revealing a levelized cost of electricity of 1.48 $/kWh, annual energy losses of about 15% of the generated energy, and tracking accuracies of 84.77% for elevation and 62.41% for azimuth angles. Although the tracking accuracy and LCOE can be enhanced further by addressing the research limitations, it is anticipated that this research will serve as a valuable reference and provide important insights into photovoltaic solar tracking systems and maximum power point tracking.