Optimal growth of structures governed by spatially stochastic dynamics arises in many scientific settings, for example in processes such as solution-based crystallization and the formation of microbial biofilms on patterned substrates or microfluidic networks. In this work, we investigate lattice growth using a two-dimensional, zero-temperature stochastic model of short-range spin interactions. Our goal is to determine how external perturbations can be optimized to steer the system efficiently toward the uniformly positive state, starting from two initial clusters of positive sites. To achieve this, we cast the problem as a Markov decision process adapted for a two-dimensional Ising model with zero-temperature dynamics. Within this framework, we compare alternative growth geometries and identify the structure of optimal strategies across three representative regimes.