This paper explores the impact of different strategies for generating initial populations on the outcomes of genetic algorithms. The primary objective of this research is to execute a genetic algorithm using various initial population generation strategies and to develop tailored recommendations for a specific business scenario. This scenario involves the use of sanitary inspections aimed at curbing the spread of food-borne disease epidemics. Through this study, we analyze how sanitary inspection activities contribute to the prevention of food-borne illnesses. The paper provides a detailed description of the problem-solving approach utilized, along with a comprehensive explanation of the genetic algorithm implementation. Moreover, it extends to a discussion on the importance of initial population selection in influencing the efficiency and effectiveness of the algorithm. The culmination of this research is a set of practical recommendations, derived from numerical analyses, intended to guide future applications of genetic algorithms in similar contexts.