## 1 Introduction  
Composite materials are seeing a growing range of numerical, analytical and experimental applications across various engineering disciplines where high stiffness, strength, and reduced structural mass is required [1, 2, 3, 4, 5]. Lightweight design methodologies, recognized as essential for developing higher performance, fuel-efficient vehicles [6, 7, 8, 9, 10, 11, 12], are being actively researched by leading automotive manufacturers and suppliers committed to minimizing mass in vehicle structures [5, 13]. Among other materials, carbon fiber-reinforced polymers (C.F.R.P.) stand out as the foremost choice for achieving lightweight structural solutions, owing to their superior vibration, fatigue, and corrosion resistance, high specific strength and stiffness, and exceptional energy absorption capabilities [14]. Consequently, CFRP composite structures are widely employed in monocque chassis designs for racing and high-performance vehicles [15], despite the limitations in manufacturing that hinder cost-effective mass production. The primary determinant of the performance of composite structures, excluding the factor of the geometry of the structure they are applied to, lies in the final mechanical properties of the C.F.R.P. material after the curing of the matrix, which are affected by factors such as the elastic properties of the individual orthotropic laminae, their orientation angles, and the stacking sequence within the laminate [1,3]. Consequently, achieving an optimal design requires a combinatorial approach to the subject, considering the extensive range of parameters affecting the behavior of the composite under specific thermomechanical loading conditions. Generally, such approaches involve varying the design variables, using advanced computational tools, and performing analysis on the resulting configurations. To achieve this target, optimization algorithms (O.A.) are usually paired with analytical or numerical models to ensure the accuracy of the computed quantities [16]. Indicative examples of such studies were performed by [PERSON] and [PERSON] (1995) [17], who optimized the stacking sequence of composite laminate plates for maximizing buckling load capacity, and [PERSON] et al. (2002) [18], who focused on determining numerically optimized stacking sequences for a simplified model of an underwater vessel. Also, recently, a single-objective genetic algorithm (G.A. ), based on the non-dominant principle, was introduced by [PERSON] et al. (2020) [16] to establish an optimization model for thin wall beams in automotive structures which used constraints such as their cross-sectional stiffness, manufacturability, and cost. Moreover, analogous research from the domain of aeronautical engineering, exemplified by the works of [PERSON] et al. (2024) [5] and [PERSON] et al. (2025) [19], further illustrates the potential advancements achievable in structural design through the coupling of these methodologies. Despite concordance between analytical model outcomes and experimental data, these studies are limited by either the parameters of constituent materials or the simplicity of geometric configurations and load case examined factors that significantly influence composite layer optimization procedure. In this study, analytically determining stress distribution proves challenging, complicating the computation of alternative lamination designs. In the case of this research, to overcome these problems, the application of optimization routines in combination with the F.E. method was implemented. This decision was derived from the advantages of combining these methods, which included achieving the main objective of the study while saving computational resources and allowing a more thorough examination of a larger portion of the design space to optimize the chassis' mass through a structured and automated method.