"Optimisation Techniques for Gas Turbine Engine Control Systems" is an external project in collaboration with the Control & Systems University Technology Centre supported by Rolls-Royce at the Automatic Control & Systems Engineering Department at the University of Sheffield. A Multi Objective Genetic Algorithm (MOGA) optimizer was implemented to optimise different jet engines' parameters influenced by several variables such as altitude measure, fuel flow, thrust and the amount of power requested by the pilot. The project's core is to improve the MOGA technique currently used by implementing a Memetic Algorithm (MA), a more elaborate Evolutionary Algorithm that consists basically of hybridizing a Genetic Algorithm (GA) with a local search technique. The resulting strategy will be implemented in an optimization tool that is currently being piloted by Rolls-Royce (RR) for controller parameter tuning. In this project, three of the most popular types of local search techniques used in MAs are successfully implemented, tested and contrasted. A detailed analysis of their performance is described, highlighting their major differences, advantages and disadvantages. These local search techniques are: Hill Climbing technique, Simulated Annealing technique and Tabu Search technique. This report provides wide background information about Genetic Algorithms and a critical evaluation of Memetic Algorithms. Finally, suggestions are made for future testing and improvements.