Genetic algorithms are variants of evolutionary algorithms that search a parameter space using an adaptive heuristic. Existing knowledge of certain biological mechanisms have largely aided in their design, which make use of principles in genetics and natural selection. This type of algorithm seeks to arrive at a optimized state according to specified criteria by exploring its (large) parameter spaces and is therefore inherently computationally intensive. Although the list of applications for genetic algorithms is growing, they are most commonly used for optimization problems. We aim to 1) give an overall introduction to the idea of genetic algorithms drawing parallels with genetics, 2) discuss cases where this algorithm class will be especially useful, and 3) demonstrate an implementation using computational tools like Databricks in Azure.