Genetic Algorithms (GAs) are emerging as powerful alternatives to traditional optimization methods which are too restrictive and CPU intensive. The field of chemical engineering offers challenging optimization and search problems. We show that, apart from being able to efficiently handle highly nonlinear, multimodal and nonlinear objective functions, GAs can also handle cases where the objective function is not clearly defined. Three case studies are presented to illustrate the effectiveness of GAs in solving complex optimization problems. The FORTRAN code along with the templates for the case studies can be obtained from the authors.