Assigning advertisements to available slots is a common challenge in digital marketing across many media platforms, including television, mobile, web, and digital streaming. Each ad slot is subject to capacity constraints, limiting the maximum number of ads it can accommodate at once. Additionally, conflict constraints prohibit certain ads from being placed together in the same slot, such as those from competing brands or due to the specific business rules. In this study, we have compared three approaches for solving this constrained ad-slot assignment problem: a simple Greedy method, a Genetic Algorithm (GA), and a Lion Pride New Genetic Algorithm (NGA). The greedy method quickly selects ads based on immediate profit but it often fails to satisfy the complex constraints optimally. The genetic algorithm addresses these constraints by simulating natural evolution, generating a population of solutions and refining them through crossover and mutation over iterations. The Lion Pride NGA enhances this further by dividing solutions into cooperative groups (prides), enabling localized constraint aware optimization and knowledge sharing between groups. Experiments demonstrate that for the variable scale problems, all methods perform adequately. However, as the number of ads, slots, and constraints increases, both GA and NGA outperform the greedy approach. Notably, NGA achieves superior constraint satisfaction and solution quality faster than GA, even in large or highly constrained environments. These results highlight the effectiveness of nature-inspired metaheuristics in navigating complex ad-slot assignment constraints, offering practical tools for real-world advertising platforms.