Abstract: This thesis presents the design methodology for the analog circuits using the genetic algorithm. Matlab is used as a tool to implement the GA (Genetic Algorithm) to analog circuits design problems as it is considered to be easy and robust with powerful features. Several analog circuits like operational amplifier circuits (inverting and non-inverting amplifiers), active filters and multistage amplifier circuits have been designed using the proposed genetic algorithm. The GA parameters are being applied to the design and examined in order to get the best parameters that match analog circuit design problems. Moreover, the effect of genetic algorithm parameters like, selection, crossover and mutation on the performance of the design process has been discussed. Specified types of GA operators and parameters were taken into account, like binary encoding, roulette wheel selection and single point crossover. The relationship between the design problem and the proposed GA is the fitness function. It is the factor which the program will make use in its convergence to the optimum solution. Comparison of results shows that GA stopping criteria for analog circuit is best to be set on minimum error (maximized fitness) and not on the number of generations, and the last is used to show how the GA is stable after finding the optimum solution. The obtained results for the different design circuits tackled by this work matched very well the theoretical one and reflect the validity of the design procedure proposed in this thesis.