Tensile and flexural tests are insufficient to identify all engineering constants required for material characterization. To address this limitation, an inverse method was developed involving several steps and software tools. The process begins with a Design of Experiments (DoE). The responses were computed iteratively in Abaqus associating each combination with numerical frequencies. Next, metamodels were constructed using Response Surface Methodology (RSM) to link the first seven modal frequencies to the engineering constants. These models were integrated into an optimization framework employing genetic algorithms (GA) to minimize differences between experimental and numerical frequencies. Key parameters, such as population size, generation size, and convergence thresholds, were optimized to improve accuracy and efficiency. Additionally, the algorithm’s version significantly impacted the precision and effectiveness of the results. Special attention was given to the Non-Dominated Sorting Genetic Algorithm (NSGA) version, whose impact on accuracy and runtime was systematically analyzed. This chapter introduces a novel strategy for inversely identifying all elastic constants in anisotropic cross-ply laminate biocomposites through experimentally measured modal analysis.