A Genetic Algorithm developer designs, codes, and tests algorithms that mimic the process of natural selection. They use techniques like mutation, crossover, and selection to solve optimization and search problems. Their work involves setting up conditions for a population of solutions to evolve towards better solutions over time. They define fitness functions to evaluate the suitability of solutions and determine their probability of reproduction. The developer continuously refines these algorithms to solve complex problems in domains such as bioinformatics, robotics, scheduling, gaming, and machine learning. They also analyze results and make necessary adjustments to improve algorithm performance.