The transparency and security that it brings in block chain technology has found a place almost everywhere one could think of—including among energy companies. Yet, the challenge is that innovative grid applications are dynamic and traditional block chain systems need to cope better in such scenarios as they were designed for static environments. It can put a burden on the intelligent grid, causing inefficiencies, and may present additional security risks in an online secure dynamic environment. In this chapter, we attempt to address the problem by introducing a new dynamic optimization method for secure block chain transactions in innovative grid applications. We address this need by embedding dynamic optimization methods within block chain protocols that respond to new state information in an intelligent grid. BT unabashedly corrects the priority of using resources efficiently and securing transactions. We design game theory to model the interactions among intelligent grid nodes and adopt a reinforcement learning technique that adjusts transaction parameters on the fly for optimal performance. Furthermore, we include a trust-based system to reduce security risks from malicious nodes on the network. We demonstrate the effectiveness of our approach through simulations and experimentally comparing it with existing block chain systems and state-of-the-art approaches.