The evolution of biological functions in protein molecules may take place on two layers that are local fragment and global domain conservations/mutations. Based on the fact that the three-dimensional structure of a protein activates its native function, this paper discusses acquiring such biological importance from comparing protein structures. Unlike the one-point search of existing methods based on various ideas, our approach utilizes the population search ability of real-coded genetic algorithm that is asynchronously parallelized. It may be useful to optimize this issue using a multiple objective evolutionary approach. In this work, we focus on maximizing two fitness functions because of the obvious trade-off. Our method as a generic structure-based alignment tool can compare all types of proteins on the two layers at a time. As the most advantageous fact, the genetic algorithm preserves local alignments as building blocks and reuses them for finding global alignments. This feature gives information on the connectivity of local fragments that often involve biological important parts, such as binding sites, active sites, etc. Robust optimization of our approach appears from experiment of protein pairs that are functionally and structurally similar/distinct. The results show that the proposed method is able to pull out the significant consideration to biological analyses.