The use of antennas for vehicle applications is growing very rapidly due to the development of modern wireless communication technology and service. Currently, an automobile antenna design using the Simple Genetic Algorithm (SGA) has been introduced. In this model, the SGA computation tool attempts to obtain the best design based on a single cost function. The automobile antenna design is a multi-objective problem. The different objectives are combined into a single cost function, each with a weight value. The results of the optimization procedure depend strongly on these weights, and thus, the designer must properly choose each weight value to get the desired optimum. Also, all the weight values must then be changed, and the entire optimization procedure must be repeated whenever the designer wants to change any single objective goal. This dissertation presents the development and application of the Nondominated Sorting Genetic Algorithm (NSGA) to design new automobile conformal antennas. The NSGA can find a set of Pareto- optimal solutions, instead of finding a single optimal solution. In multi-objective optimization problems, one may not find a single best solution. There may be many solutions, which are considered better with respect to all objectives. The Pareto-optimum solutions are a set of compromise solutions based on a comparison with each objective. The NSGA searches the Pareto- optimal solutions by using a fitness assignment process and a sharing process. A set of Pareto- Optimum automobile conformal antenna geometries for FM radio and GPS/SDARS systems using the NSGA is produced. The use of antennas for vehicle applications is growing very rapidly due to the development of modern wireless communication technology and service. Currently, an automobile antenna design using the Simple Genetic Algorithm (SGA) has been introduced. In this model, the SGA computation tool attempts to obtain the best design based on a single cost function. The automobile antenna design is a multi-objective problem. The different objectives are combined into a single cost function, each with a weight value. The results of the optimization procedure depend strongly on these weights, and thus, the designer must properly choose each weight value to get the desired optimum. Also, all the weight values must then be changed, and the entire optimization procedure must be repeated whenever the designer wants to change any single objective goal. This dissertation presents the development and application of the Nondominated Sorting Genetic Algorithm (NSGA) to design new automobile conformal antennas. The NSGA can find a set of Pareto- optimal solutions, instead of finding a single optimal solution. In multi-objective optimization problems, one may not find a single best solution. There may be many solutions, which are considered better with respect to all objectives. The Pareto-optimum solutions are a set of compromise solutions based on a comparison with each objective. The NSGA searches the Pareto- optimal solutions by using a fitness assignment process and a sharing process. A set of Pareto- Optimum automobile conformal antenna geometries for FM radio and GPS/SDARS systems using the NSGA is produced.