The purpose of this work is to demonstrate the advantages of an optimization framework to determine the best design for a large compressed air system. The framework allows the user access to a wide range of standard, pre-built optimization algorithms and help in selecting which algorithm is most appropriate for a specific problem. The framework provides development tools for users to add new algorithms to it. The optimization framework is applied to a practical problem involving the selection and design of equipment in a compressed air system for industrial plant use. Such a system consists of a centrifugal compressor, wind turbine (as a power plant), heat exchanger, pressure vessel, pump, piping, tubing, valves, motors, and a platform upon which all the equipment rests. Each piece of equipment is represented by its own software which is capable of performing design and costing calculations. All the software components for the equipment are brought together using specialized integration software. This creates the ability to use the optimization framework over the entire system. The objective of the problem is, given an upper bound on total capital cost of all the equipment, to maximize reserve capacity of air to the plant. Three algorithms were compared for this particular problem and it is shown that there is a tradeoff between algorithm effectiveness (at meeting the objective) and computational cost. It is further shown that there is a large return on investment for using such tools.