<p>The rapid growth of electric vehicles (EVs) is reshaping transportation but also creating substantial challenges for electrical grid stability due to fluctuating and high charging demands. This study aims to address these challenges by conducting a comprehensive meta-analysis of demand response management (DRM) for EVs within smart grids, explicitly integrating both optimization and security perspectives. The novelty lies in combining a systematic taxonomy of EV modeling, load forecasting, and optimization strategies with an in-depth review of encryption, authentication, and consensus protocols that ensure trusted energy transactions. Methodologically, the paper synthesizes existing studies, benchmarks DRM mechanisms against trust and performance criteria, and presents a case study integrating blockchain with reinforcement learning to achieve secure and optimal DRM. The findings show that such integration can enhance grid reliability, enable verifiable energy trading, and improve demand-side flexibility. Policy implications include the need for regulatory frameworks that mandate transparent, tamper-evident data infrastructures and incentivize secure, optimized EV charging coordination, contributing directly to UN Sustainable Development Goals on clean energy and sustainable cities. Unlike prior surveys that have treated optimization and security in EV demand response management (EVDRM) separately, this study explicitly integrates the two perspectives into a unified taxonomy and research roadmap. By systematically contrasting our framework with existing surveys, we highlight overlooked vulnerabilities, trade-offs in blockchain scalability, and reinforcement learning (RL) applicability, thereby positioning this work as a distinctive contribution to advancing secure, scalable, and optimized EVDRM.</p> Graphical abstract <p></p>