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The main purpose of this study was to combine the currently separate objectives of aerodynamic performance and manufacturing efficiency, then find an optimal point of operation for both objectives. An additional goal of the study was to explore the effects of changes in design features, the position of the spars, and analyse how the changes influenced the optimal operating conditions. A machine-learning approach was taken to combine and model the gathered aero-manufacturing data, and a multi-objective optimisation approach utilising genetic algorithms was implemented to find the trade-off relationship between optimal target objectives (mission performance and manufacturability). The main achievements and findings of the study were: The study was a success in building a machine-learning model for the combined aero-manufacturing data utilising software library XGBoost; multi-objective optimisation, which did not include spar positions as a variable found the trade-off region between high manufacturability and high mission performance, with choices that offered reasonably high values of both; there was no clearly identified correlation between a small change in spar position and the target objectives; multi-objective optimisation with spar positions resulted in a trade-off relationship between target objectives, which was different from the trade-off relationship found in optimisation without spar positions; multi-objective optimisation with spar positions also offered more flexibility in the choice of manufacturing processes available for a given design; and the range of bump amplitudes for solutions found by multi-objective-optimisation with spar positions was lower and more focused than those found by optimisation without spar positions.