A two-population Genetic Algorithm for constrained optimization is exercised and analyzed. One population consists of feasible candidate solutions evolving toward optimality. Their infeasible but promising offspring are transferred to a second, infeasible population. Four striking features are illustrated by executing challenge problems from the literature. First, both populations evolve essentially optimal solutions. Second, both populations actively exchange offspring. Third, beneficial genetic materials may originate in either population, and typically diffuse into both populations. Fourth, optimization vs. constraint tradeoffs are revealed by the infeasible population.