Wind farm control is an area of research that looks to maximise the performance of individual but most importantly the collective performance of wind farms. The performance of turbines located in a wind farm is heavily dependent on the effects of turbine wakes, and improving the efficiency of wind farms by mitigating the wake effects is an ongoing and promising field of research. Simulating the wake effects of a turbine is generally done with computationally expensive physics based modelling techniques like CFD simulations. The computational complexity of these models limits the scope of optimisation that can be performed on wake mitigating controls strategies such as dynamic induction control. A substitute engineering model like the free vortex wake model could provide better insight into dynamic induction control by virtue of its computational efficiency allowing a wider optimisation parameter space. In this thesis an optimisation framework based on a 2D implementation of the free vortex wake model and genetic algorithm optimisation is used to investigate dynamic induction control for a simple two turbine wind farm beyond the research that has been done up to this point. While yet to be confirmed with higher fidelity simulations, initial results corroborate earlier findings for simple dynamic induction signals and additionally indicate that dynamic induction control could benefit from multiple harmonics. The optimisation framework achieves its goal in allowing a wider parameter space to be searched for optimisation, even on consumer grade desktop hardware, and shows potential as a tool for further investigation of dynamic induction control.