Recent advances in surface reconstruction from 3D point cloud data have significantly improved available techniques used for generating accurate digital models of physical objects. Among these, new methods have been proposed for generating Non-Uniform Rational B-Splines (NURBS) surfaces from 3D imaging data using machine learning algorithms. This study introduces a novel approach to the three-dimensional modal reconstruction of the human corneal surface using an evolutionary algorithm-based method. The proposed workflow employs an evolutionary algorithm to optimize the zonal reconstruction of the anterior and posterior corneal surfaces, ensuring precise alignment and smoothness of the resulting geometry. Genetic algorithms are particularly effective for optimizing NURBS curves with Galapagos software, making them well-suited for tasks such as minimizing approximation errors relative to reference points, enhancing curve smoothness, or achieving specific configurations in applications like parametric design, engineering, or manufacturing. The process begins with an initial population of NURBS curves, which are generated with randomized configurations of control points, weights, and parameters. To enhance convergence, curves can be based on an initial baseline design. All data used in this study was obtained from the IBERIA BIOBANK database (Universidad Miguel Hernández de Elche, OFTARED–Instituto de Salud Carlos III). The spatial point cloud data was integrated into a parametric workflow using Grasshopper software. The inclusion of an evolutionary algorithm introduces a significant layer of optimization to generate NURBS surfaces. This method adapts to the topographic data, iteratively refining the corneal model based on fitness criteria. The evolutionary algorithm operates over multiple generations, evaluating, selecting, and reproducing new NURBS curves. The process continues until a stopping criterion is reached, such as a maximum number of generations or a predefined error threshold. This study presents a novel framework for corneal modelling using an evolutionary algorithm-based method integrated with Galapagos software. By combining parametric design and advanced optimization techniques, this methodology establishes a foundation for innovative diagnostic applications in ophthalmology. The potential benefits include improved understanding and management of corneal diseases, contributing to advancements in clinical practices.