Artificial Intelligence (AI) increasingly supports decision-making in high-stakes settings like hiring and healthcare, offering efficiencies and scalability. However, AI systems also risk replicating and exacerbating structural inequalities encoded in training data and engineering choices. AI is vulnerable to various forms of algorithmic discrimination: individual, group-based, and the particularly concerning intersectional discrimination, where disadvantage arises from the interaction of multiple protected characteristics (e.g., race, gender, age). Existing fairness testing methods largely ignore these complex and nuanced forms of bias and frequently depend on incomplete, imbalanced real-world datasets, which compromise testing accuracy. This paper presents a conceptual overview utilizing metaheuristic algorithms to automate and systematize fairness testing. Fundamentally, fairness testing is reframed as an optimization problem, leveraging the search and exploration properties of metaheuristics to synthesize targeted test cases that maximize the probability of identifying discriminatory behavior. This method provides a powerful, scalable, and efficient means of detecting pragmatic indications of bias at the individual, group, and intersectional levels, addressing limitations often found in traditional testing. This study contributes a novel conceptual approach to fairness testing, aiming to foster fairer, more transparent, and socially responsible AI systems.