All the work presented in this article was performed with the in-house implementation of a single and multi-objective GA engine called EVOLVE. As a versatile and modular Python code for peptide and protein sequence optimization, EVOLVE was successful in the optimization of a biomimetic peptidic scaffold for the fixation of CO2 and in the engineering of a highly thermostable metalloprotein. It also served in the elaboration of training sets for enhanced machine learning models of molecular properties. For the compositional optimizations mentioned above, side chain rotamer libraries were used in order to restrict the search space to discrete sets of residue conformations. In contrast here, EVOLVE is extended into a complete in silico optimizer of a peptide structure (including both the backbone conformation and side chain dihedrals) with a fixed amino acid sequence whose degrees of freedom therefore cover a huge space. In the gas phase at near zero temperature, the objective function is nothing more than the potential energy as a function of atomic coordinates, meaning that the lower the energy (fitness), the better the structure. Such an 'ab initio' peptide folder is capable of exploring the low-lying LM or reaching the GM of the PES, which is particularly relevant for assigning 3D structures to measured IR spectra. In addition, it also provides an exhaustive search that enables a quality test of the method used to describe the PES.