In the last few years, evolutionary computing (EC) approaches have been successfully used for many real world optimization applications in scientific and engineering areas. One of these areas is computational nanoscience. Semi-empirical models with physics-based symmetries and properties can be developed by using EC to reproduce theoretically the experimental data. One of these semi-empirical models is the Valence Force Field (VFF) method for lattice properties. An accurate understanding of lattice properties provides a stepping stone for the investigation of thermal phenomena and has large impact in thermoelectricity and nano-scale electronic device design. The VFF method allows for the calculation of static properties like the elastic constants as well as dynamic properties like the sound velocity and the phonon dispersion. In this paper a parallel genetic algorithm (PGA) is employed to develop the optimal VFF model parameters for gallium arsenide (GaAs). This methodology can also be used for other semiconductors. The achieved results agree qualitatively and quantitatively with the experimental data.