In this work, we have presented a GA based search method to efficiently sample low-energy structures of peptides and its implementation in our in-house code EVOLVE. Rather than aiming for a full first-principles exploration, we argue that resorting to more expedient surrogates allows to significantly reduce the computational expense in the screening of candidate structures to be later re-optimized at the ab initio level. This is motivated by the fact that coordinates of local minimum candidates are in general well approximated by surrogates, while getting reliable energies is the main dif-ficulty.