<p>Achieving optimized structural configurations while considering Poisson ratio constraints and anisotropic effects is a critical challenge in material design and additive manufacturing. This study presents a multi-objective evolutionary optimization framework that systematically refines geometries through an iterative Finite Element Analysis (FEA)-driven approach. The algorithm generates an initial population of 100 geometries, ensuring compliance with connectivity and boundary constraints, and evolves them through mutation-based modifications across multiple generations. Mesh convergence analysis was performed, and the optimal mesh size was selected at the point where results stabilized. The evolutionary algorithm selects the 10 best-performing geometries per generation based on multiple objective criteria, iteratively refining structural efficiency while avoiding misleading crossover operations. Results indicate that the algorithm effectively aligns stress trajectories with manufacturing constraints, particularly in additive manufacturing, where anisotropy significantly influences mechanical performance. The anisotropy-aware designs exhibit superior load distribution by actively reducing the presence and impact of mechanical anisotropy, thereby minimizing adverse stress orientations relative to the build plate when compared to non-optimized, anisotropy-affected structures. These findings suggest that incorporating anisotropy constraints within evolutionary optimization enhances both structural performance and manufacturability. Future research will focus on refining mutation strategies and experimentally validating computational predictions through physical production and mechanical testing.</p>