To aid in the investigation of new simultaneous optimisation strategies for exible vehicles and their control systems, a two-dimensional aerofoil optimisation which demands minimal computational effort is studied. The aeroservoelastic system consists of a two-dimensional, potential flow over a deforming aerofoil; an actively controlled, but saturated compliant trailing edge; a dynamic observer that uses a series of pressure sensors on the aerofoil; and a heave/pitch linear spring model. Although computationally simple, the design allows for optimisation over multiple disciplines: the structure can be designed by varying the stiffness of the springs; the control architecture through weightings in a LQR controller; the observer by means of the placement of pressure sensors; and the aerodynamics via the shaping of the compliant trailing edge. Optimising the weight and a metric of performance over all these fields simultaneously is compared to a sequential methodology of optimising the open-loop characteristics first and subsequently adding a closed-loop con-troller. Parametrisation of the design vector and variable selection often require user input and are fixed during optimisation. Our research aims to automate this process. Further-more, we investigate whether varying the parametrisation and number of design variables during the optimisation can lead to improvements in the final design. To accomplish this, a new basis for the design vector is created via Proper Orthogonal Decomposition (POD) using the trajectories of initial optimisation paths as a “training set". This parametrisation is shown to make the optimisation more robust with respect to the initial design, and facilitate an automated variable selection methodology. This variable selection allows for the dimension of the problem to be reduced temporarily and it is shown that this makes the optimisation more robust.