Abstract: The theory of evolution is an instrument for optimization in the process of solution for problems of different nature with the main goal creating a population of eventual solutions using some of the most characteristic peculiarities of nature – heredity, changeability, selection and so on. Most of the current research on evolutionary optimization has concentrated on design of naturally inspired evolutionary algorithms to search for nondominated solutions. This is the inspiration behind this work to develop an evolutionary algorithm based on plant and animal breeding. The area of Selective Breeding is emerging as an active and attractive field involving models, techniques and applications of greater diversity. In this study, a novel evolutionary algorithm based on selective breeding is developed. Bin packing of N = n2 pieces in a massively parallel computing environment of n2 processors operating in SIMD mode is used for evaluating the performance of SBA algorithm. Results are compared with other packing algorithms reported in the literature. Selective Breeding Algorithm (SBA) is good problem solving technique for the Bin Packing (BP) application and it gives comparatively optimal result with other packing algorithms.