The proliferation of online misinformation demands the development of highly accurate and computationally efficient automated systems for Fake News Detection. A primary impediment to system performance is the high dimensionality of textual features derived from techniques like TF-IDF, making optimal Feature Selection a critical step. This paper presents a detailed comparative experimental study of two prominent bio-inspired evolutionary metaheuristics, the Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) used as wrapper-based FS techniques for FND. The methodologies were rigorously tested across two distinct textual datasets: the complex, large-scale FakeNewsNet corpus and a moderate-scale general news dataset. The feature sets, once optimised, were evaluated using six standard Machine Learning (ML) classifiers. The GA-based FS approach, emphasising global exploration, achieved state-of-the-art accuracy of 99.91% with the Random Forest classifier on the FakeNewsNet dataset. In contrast, the PSO-based FS approach, valued for its rapid convergence, yielded a maximum accuracy of 93.29% with the Support Vector Machine (SVM) on the general news dataset. This analysis provides empirical evidence of the intrinsic trade-off between the algorithms: GA is superior for maximising accuracy in high-dimensional, complex textual spaces, while PSO offers a more efficient and practical solution for resource-constrained or moderate-scale FND tasks. The study confirms that evolutionary computation provides a robust, effective pathway for significantly enhancing ML classifier performance in this critical domain.