Many real world problems are multi-objective in nature. There are many methods that solve this kind of optimization problem. They can be classi- fied into two classes: genetic algorithms and classical methods. Instead of one solution, both methods find a whole set of solutions. Genetic algorithms seem to attract many researchers due to their robustness and also to their degree of generality, while others prefer methods based on classical optimization algorithms. The choice of one approach over another depends much on the nature of the problem. If we have enough information about the optimization problem, it is better to use methods based on classical optimization algorithms. If not, it is better to use multi-objective genetic algorithms or evolution algorithms. In this thesis we consider a design optimization problem. The task is to design a jet engine by specifying certain parameters such as pressure ratio in the turbine. We run a flight simulation in Matlab, which allows us to calculate the fuel consumption as well as the engine weight for different input parameters. Here the input parameters are decision variables while the objective functions are the fuel consumption and the engine weight. We applied two multi-objective evolution algorithms and one method based on classical optimization algorithms.