Aerodynamic design of an aero-engine fan blade is a multi-step process with multi-variables. The general purpose in aerodynamic design is to obtain proper blade angles and flowpath geometry providing the necessary pressure ratio with maximum efficiency, while respecting the structural and aerodynamic constraints. The throughflow design in aerodynamic design procedure is a key step where one can obtain a basic aero-design which generally fixes 80% to 90% of the final fan geometry, by adjusting parameters like blade exit angle distribution, solidity, hub and shroud contour, meridional chord length, etc. Throughout this procedure, the aim of the designer is to obtain an optimum (i.e. light, reliable and robust) system with highest efficiency. Among optimization methods, zero order methods are reported to fit best for turbomachinery problems, due to their good performance in discrete and non-differentiable problems and their ability to find the global optimum. Genetic algorithm is the most widely used optimization method in turbomachinery optimization. Methods inspired by swarm intelligence are reported as promising global optimizers, whereas, to the author’s knowledge, there are no reported studies that employs such algorithms in turbomachinery throughflow optimization. These methods can find the neighborhood that provides the globally optimum design, rather than exactly finding the global design. This drawback is overcome by hybridizing genetic/swarm inspired algorithms by first order (gradient based) methods. Within this aspect, the present study focuses on developing genetic and swarm inspired algorithms hybridized with gradient based algorithms to find the optimum throughflow design of a transonic aero-engine fan module.