In deregulated electricity markets, power generators must develop optimal bidding strategies to maximize their expected revenue while mitigating risks associated with market volatility. This paper presents a comprehensive approach to formulating an optimal bidding strategy for a generator in the day-ahead electricity market. The proposed strategy considers key factors such as historical market price distributions and operational constraints. A probabilistic model based on the normal distribution is used to analyze historical data and estimate price trends. The objective function is designed to maximize the generator’s expected profit while adhering to capacity limits and market-clearing condition. To enhance the efficiency of the bidding strategy, the Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic algorithm is employed for optimization. Simulation results demonstrate the effectiveness of the proposed strategy in enhancing revenue potential while ensuring market competitiveness. The findings provide valuable insights for market participants and policymakers in designing efficient bidding frameworks that improve electricity market efficiency.