Increasingly, the use of geometric modelling techniques in Applied Ophthalmology is significant in the characterization of important pathologies of the cornea, such as Keratoconus. This article presents a novel method for the geometric reconstruction of the corneal surface from optical topography using a genetic algorithm. Traditionally, mathematical programming methods such as the least squares method have been used to obtain the coefficients of the corneal surface function, such as Navarro model or Zernike polynomials. This new method uses non-dominated multivariable genetic algorithm optimization to obtain the surface function coefficients from the point cloud obtained with corneal topographer device. Once the reconstruction is performed, the surface is represented using CAD software, and morphogeometric parameters are obtained. The experimental sample consisted in 33 healthy patients eyes, aged from 11 to 63, and without previous ocular surgeries or pathologies. Topographic data were obtained using a Scheimpflug Sirius tomographer (CSO, Italy). The computational optimization was executed under Matlab software environment (Mathworks, USA). The new method provides a lower mean squared error (MSE) than those obtained by the least squares or the nonlinear programming algorithms. Thus, the morphogeometric parameters obtained from the patient's corneas fit better, allowing for a better analysis of real clinical conditions.