Evolutionary design optimization for improving the performance of real world objects, like e.g. car shapes in the context of aerodynamic efficiency, usually depends on a well balanced combination of representation, optimizer and design evaluation method. Shape representation requires a fair trade-off between minimum number of design parameters and design flexibility which likewise guarantees a good optimization convergence while allowing manifold design variations. Recently, shape morphing methods have gained increased attention because of their capability to represent complex shapes with a reasonable number of parameters, especially powerful if coupled with numerical simulations for measuring design performance. Free-form deformation, as prominent shape morphing representative, relies on an initial grid of control points, the control volume, which allows the modification of the embedded shape. The set-up of the control volume is a crucial process which in practice is done manually based on the experience of the human user. Here, a method for the automated construction of control volumes is suggested based on a proposed measure E_CV which relies on the concept of evolvability as a potential capacity of representations to produce successful designs in a reasonable time. It is shown for target shape matching experiments that optimizations based on E_CV-tuned control volumes provide a significantly better performance in design optimization.