This dissertation presents a novel Bi-objective Multi-population Genetic Algorithm (BMPGA) for multimodal optimization problems. BMPGA is distinguished by its use of two separate but complementary fitness objectives designed to enhance the diversity of the overall population and exploration of the search space. This is coupled with a multi-population strategy and a clustering scheme, both of which together focus selection pressure within sub-populations, resulting in improved exploitation of promising optimum areas as well as effective identification and retention of potential optima. The practical value of BMPGA is demonstrated in several applications including optimization of benchmark multimodal functions and detection of imagery ellipses. BMPGA is compared with widely used algorithms and exhibits solid advantages over them. BMPGA is also extended to the segmentation of microscopic cells, which is a necessary first step of many automated biomedical image processing procedures.

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