Genetic algorithms (GAs) are search and optimization tools, which work differently compared to classical search and optimization methods. Because of their broad applicability, ease of use, and global perspective, GAs have been increasingly applied to various search and optimization problems in the recent past. In this paper, a brief description of a simple GA is presented. Thereafter, GAs to handle constrained optimization problems are described. Because of their population approach, they have also been extended to solve other search and optimization problems efficiently, including multimodal, multiobjective and scheduling problems, as well as fuzzy-GA and neuro-GA implementations. The purpose of this paper is to familiarize readers to the concept of GAs and their scope of application.