This paper presented a multicriteria parametric optimization method based on enhanced weighted TOPSIS method. The approach aimed to mitigate the impact of constraints in multicriteria parametric optimization, distinguishing itself from universal methods such as the direct method, response surface method, and traditional weighted TOPSIS method. This work focused on minimizing the computer time required for solving the complex problem of reducing the weight of frame-shell load-bearing structures while concurrently ensuring structural strength and rigidity. To enhance computational efficiency, the devised method incorporates an iterative process of the traditional weighted TOPSIS method integrated with a genetic algorithm. This integration seeks to mitigate the influence of criteria numbers and objective function weighting coefficients on the optimization outcome by utilizing numerical results from the weighted TOPSIS method. The need for recalculations is obviated through the utilization of a pre-established database containing computed individuals. Moreover, to mitigate the influence of population disparities on the optimization outcome, global extreme individuals are introduced to foster convergence. This systematic approach ensures both computational expediency and robust optimization results in the context of the genetic algorithm framework. The efficacy of the proposed methodology was evaluated through a comparative analysis of optimization outcomes achieved using the proposed approach and those derived from conventional methods applied to the Humdinga amphibious vehicle chassis. Upon analysis of the calculation results, it can be inferred that the proposed method facilitated a substantial reduction in structural weight, exemplified by the underbody panel exhibiting a mass 51% lower than that of the original design. In comparison with widely adopted methods for multicriteria parametric optimization, such as the direct method and the response surface method, a more pronounced reduction in body weight was achieved by the proposed method (44% for the direct method, and 42% for the response surface method). In contrast to the traditional weighted TOPSIS method, the proposed multicriteria parametric optimization method exhibited substantial decrease in computational time for solution attainment (75% reduction in computer time).