<p>In the present work, an oppositionally driven crisscross gravitational search approach (OCcGSA) is proposed to solve the economic load dispatch (ELD) problem. The ELD problem is a highly nonlinear and complex problem due to constraints like ramp rate, valve point loading effect (VPLE), ramp-rate restrictions, and prohibited operating zones (POZ). The objective of the work is to minimize the total operating cost during power generation from a given set of generating units. The novelty of the proposed method lies in hybridization of the gravitational search algorithm (GSA) with criss-Cross optimization and opposition-based learning. Additionally, a new mass equation is introduced to further enhance the algorithm's diversity. The proposed technique maintains a good balance between exploration and exploitation in the entire search space, avoids local stagnation and maintains population diversity. The effectiveness of the OCcGSA is assessed using benchmark functions and real-time electric power system problems. The simulation and comparison of results advocate that the OCcSGA gives better or more competitive results than other methods in the literature. OCcGSA shows significant savings in thermal unit operating costs, ranging from 0.0674$/h to 6.3$/h. OCcGSA gives a better result; the standard deviation of OCcGSA is 99.97% better than GSA. Statistical checks are provided by the Wilcoxon-score rank test. The robustness and efficiency of OCcGSA are provided by plotting box plots and convergence plots.</p>