MOTIVATION: The ability to measure the phenotype of millions of different genetic designs using Massively Parallel Reporter Assays (MPRAs) has revolutionized our understanding of genotype-to-phenotype relationships and opened avenues for data-centric approaches to biological design. However, our knowledge of how best to design these costly experiments and the effect that our choices have on the quality of the data produced is lacking. RESULTS: In this article, we tackle the issues of data quality and experimental design by developing FORECAST, a Python package that supports the accurate simulation of cell-sorting and sequencing-based MPRAs and robust maximum likelihood-based inference of genetic design function from MPRA data. We use FORECAST's capabilities to reveal rules for MPRA experimental design that help ensure accurate genotype-to-phenotype links and show how the simulation of MPRA experiments can help us better understand the limits of prediction accuracy when this data are used for training deep learning-based classifiers. As the scale and scope of MPRAs grows, tools like FORECAST will help ensure we make informed decisions during their development and the most of the data produced. AVAILABILITY AND IMPLEMENTATION: The FORECAST package is available at: https://gitlab.com/Pierre-Aurelien/forecast. Code for the deep learning analysis performed in this study is available at: https://gitlab.com/Pierre-Aurelien/rebeca.