Many proteins involved in human proteinopathies exhibit complex energy landscapes with multiple thermodynamically-stable and semi-stable structural states. Landscape reconstruction is crucial to understanding functional modulations, but one is confronted with the multiple minima problem. While traditionally the objective for evolutionary algorithms (EAs) is to find the global minimum, here we present work on an EA that maps the various minima in a protein's energy landscape. Specifically, we investigate the role of initialization of the initial population in the rate of convergence and solution diversity. Results are presented on two key proteins, H-Ras and SOD1, related to human cancers and familial Amyotrophic lateral sclerosis (ALS).