This comprehensive study investigates the optimization of Electric Vehicle (EV) aggregators' efficiency in the dynamic power market environment. Through advanced mathematical modeling, extensive simulations, and rigorous analysis, we explore a multitude of strategies to enhance the performance and economic benefits of EV aggregators. Our research encompasses the development of sophisticated optimization models that consider a wide array of constraints and variables influencing aggregator efficiency. These include time-varying electricity prices, diverse EV fleet compositions, battery degradation factors, and grid demand fluctuations. The study employs state-of-the-art mixed-integer linear programming techniques and incorporates stochastic elements to account for market uncertainties. Our results provide nuanced insights into how EV aggregators can be effectively integrated into the power market, offering detailed, practical recommendations for improving aggregator efficiency and overall system performance. This research significantly contributes to the growing body of knowledge on sustainable energy management and the seamless integration of electric vehicles into smart grid systems, paving the way for more resilient and efficient power markets.