The applicably of multi-objective optimization to ellipsometric data analysis is presented and a method to handle complex ellipsometric problems such as multi sample or multi angle analysis using multi-objective optimization is described. The performance of a multi-objective genetic algorithm (MOGA) is tested against a single objective common genetic algorithm (CGA). The procedure is applied to the characterization (refractive index and thickness) of planar waveguides intended for the production of optical components prepared sol-gel derived organic-inorganic hybrids, so-called di-ureasils, modified with zirconium tetrapropoxide, Zr(OPr(n))(4) deposited on silica on silicon substrates. The results show that for the same initial conditions, MOGA performs better than the CGA, showing a higher success rate in the task of finding the best final solution.