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The successive generations of consensus algorithms progressively displaced the performance bottleneck of blockchains to the execution layer. Recent works address the execution performance bottleneck by parallelizing the execution of non-conflicting transactions. Historically, execution closely followed the consensus-level transaction ordering determined by validators, a practice highly susceptible to Maximal Extractable Value (MEV) exploitation. Conversely, recent academic proposals introduce rigid fair-ordering protocols that are bound to severely restrict transaction reordering at the execution layer. Parallel execution frameworks optimize the sequencing layer, which lies between consensus and execution and assembles transaction batches from committed consensus blocks and transmits them to execution workers to maximize both execution parallelism and realized transaction fees. To achieve this optimization, current sequencing implementations may defer transactions. Importantly, these implementations do not maintain order-fairness properties. To the best of our knowledge, preserving these properties currently requires sequential execution, which would drastically reduce both profitability and performance. In this work, we address the tension between validator profit and order fairness using a dynamic optimization framework. We introduce a blockchain-agnostic model for transaction sequencing in a continuous setting where block sequencing and execution run concurrently. Consequently, when sequencing cannot be completed within the available time window, our framework dynamically returns its best intermediate result. Within this framework, we propose an anytime genetic algorithm that utilizes gas prices, object sets, and predicted execution times to optimize schedules. We also augment this algorithm to optionally maintain fair-ordering. We evaluate our approach with real-world datasets from Sui and Ethereum, and demonstrate that it increases validator profit by approximately 15% and accelerates congestion relief speed by up to 58%. Furthermore, we quantify the impact of fair-ordering constraints, showing that they can reduce validator profit by 50% to 60% during periods of high congestion. We provide the first evidence that enforcing strict fair ordering might effectively nullify the advantages of advanced sequencing.