This research study focuses on solving the deployment planning problem for UAV-BSs using Multi-Objective Evolutionary Algorithms (MOEAs). The main research objectives encompass gridbased modelling of the target area, investigating evolution parameters, and evaluating algorithm performance in diverse deployment scenarios. Cost, coverage, and interference are considered as objectives along with specific constraints to generate optimal deployment plans. The solution incorporates objective decision support for selecting the best solution among the Pareto front. The research also accounts for parameter initialization and UAV network heterogeneity. Through comprehensive evaluations, the proposed solution demonstrates computational efficiency and the ability to generate satisfactory deployment plans. The study recommends using NonDominated Sorting Genetic Algorithm-II (NSGA-II) for optimal performance. The research also incorporates a fitness approximation technique to reduce computational time while maintaining solution quality. The findings provide valuable insights and recommendations for efficient and balanced deployment planning. However, the research acknowledges limitations and suggests future enhancements. Overall, this research contributes to the field by establishing a foundation for robust and practical deployment plans, guiding future advancements. Future research should focus on addressing identified limitations to enhance applicability and effectiveness in real-world deployment scenarios.