We present a new methodology, based on a combination of genetic algorithms and image morphometry, for matching the outcome of a Monte Carlo simulation to experimental observations of a far-from-equilibrium nanosystem. The Monte Carlo model used simulates a colloidal solution of nanoparticles drying on a solid substrate and has previously been shown to produce patterns very similar to those observed experimentally. Our approach enables the broad parameter space associated with simulated nanoparticle self-organization to be searched effectively for a given experimental target morphology.