Multimodal optimization represents a significant and ongoing challenge within the broader field of optimization, particularly due to the presence of multiple global and local optima within a complex search space. Unlike unimodal problems that focus on a single optimal solution, multimodal problems require algorithms to locate and maintain a diverse set of high-quality solutions across various regions of the landscape. This characteristic reflects many real-world scenarios, such as engineering design, robotics, and bioinformatics, where multiple viable solutions can coexist. Traditional optimization algorithms often struggle in such settings, as they tend to converge prematurely to a single optimum and lack mechanisms for diversity preservation. In this paper, we propose a novel niching-based Genetic Algorithm (GA) tailored specifically for multimodal optimization problems. The proposed algorithm dynamically forms niches based on the spatial distribution of individuals in the population, enabling the preservation and evolution of multiple optima simultaneously. To ensure that niches are maintained effectively, the genetic operators are strategically modified to minimize disruption to niche structure during crossover and mutation. Extensive experiments conducted on standard multimodal benchmark functions demonstrate that our approach consistently outperforms existing methods in both convergence speed and solution diversity. The results validate the algorithm’s robustness and its practical potential in solving complex multimodal problems.