Gene therapies have the potential to treat disease by delivering therapeutic genetic cargo to disease-associated cells.One limitation to their widespread use is the lack of short regulatory sequences, called promoters, that differentially induce the expression of delivered genetic cargo in target cells, minimizing side effects in other cell types. Such cell-type-specific promoters are difficult to discover using existing methods, requiring either manual curation or access to large datasets of promoter-driven expression from both targeted and untargeted cells. Model-based optimization (MBO) has emerged as an effective method to design biological sequences in an automated manner, and recent promoter design methods use simple MBO techniques. However, these methods have only been tested using large training datasets that are expensive to collect, and focus on designing promoters for markedly different cell types, overlooking the complexities associated with designing promoters for closely related cell types that share similar regulatory features. Therefore, we introduce a comprehensive guide to utilizing MBO for designing promoters in a data-efficient manner, with a specific emphasis on discovering promoters for similar cell types. We focus on using conservative objective models (COMs) for MBO, and highlight practical considerations such as best practices for improving sequence diversity, getting estimates of model uncertainty, and finally choosing the optimal set of sequences for wetlab validation. We choose three relatively similar blood cancer cell lines and show that our approach is indeed capable of discovering many novel cell-type-specific promoters after experimentally validating the designed sequences.