In this paper we tackle the dynamic stacking problem by introducing a framework for incremental online optimization. The dynamic stacking problem features continuous uncertain arrival and delivery of blocks via a crane controlled by the solver. The problem is implemented as a discrete event simulation and the solver runs asynchronously. We develop a framework that can use our existing offline solver for the dynamic stacking problem and turn it into an online solver capable of incrementally updating optimized plans. We test our framework by comparing to our previously published iterative approach as well as a rule based baseline solver on a diverse set of problem instances. Using the new framework, the solver improves our key performance indicators across the benchmark instances. We also investigate the reasons for the performance differences both in the aggregate as well as the level of individual simulation runs. The framework not only works well on this specific stacking problem, but is general enough to be used in many online dynamic optimization problems.