One of the primary objectives of voice research is to better understand the biomechanics of voice production and how changes in properties of the vocal folds (VFs) affect voice ability and quality. Synthetic VF models provide a way to observe how changes in geometry and material property affect voice biomechanics. This thesis seeks to evaluate an approach of using a genetic algorithm to design synthetic VF models in three ways: first, through the development of a computationally cost-effective 3D vocal fold model; second, by creating and optimizing a variation of this model; and third, by validating the approach. To reduce computation times, a user-defined function (UDF) was implemented in low-fidelity 2D and 3D computational VF models. The UDF replaced the conventional meshed fluid domain with the mechanical energy equation. The UDF was implemented in the commercial finite element code ADINA and verified to produce results that were similar to those of 2D and 3D VF models with meshed fluid domains. Computation times were reduced by 86% for 2D VF models and 74% for 3D VF models while core vibratory characteristic changes were less than 5%. The results from using the UDF demonstrate that computation times could be reduced while still producing acceptable results. A genetic algorithm optimizer was developed to study the effects of altering geometry and material elasticity on frequency, closed quotient (CQ), and maximum flow declination rate (MFDR). The objective was to achieve frequency and CQ values within the normal human physiological range while maximizing MFDR. The resulting models enabled an exploration of trends between objective and design variables. Significant trends and aspects of model variability are discussed. The results demonstrate the benefit of using a structured model exploration method to create models with desirable characteristics. Two synthetic VF models were fabricated to validate predictions made by models produced by the genetic algorithm. Fabricated models were subjected to tests where frequency, CQ, and sound pressure level were measured. Trends between computational and synthetic VF model responses are discussed. The results show that predicted frequency trends between computational and synthetic models were similar, trends for closed quotient were inconclusive, and relationships between MFDR and sound pressure level remained consistent. Overall, while discrepancies between computational and synthetic VF model results were observed and areas in need of further study are noted, the study results provide evidence of potential for using the present optimization method to design synthetic VF models.