Biomanufacturing of cell and gene therapies is a complex process, hampered by variability in yield, quality, and scalability of manufacturing platforms. Production parameters can be key drivers for vector quality, quantity and cost, making them prime process optimization targets. However, assessing all possible variables and understanding their interactions with product output and each other can be a convoluted and time-intensive task when using traditional one-factor-at-a-time (OFAT) process optimization strategies. A high-throughput, 96-well format virology platform using transduction-based quantification of luciferase reporters allows for rapid quantification of various produced viral vectors. We developed transfection-based production and quantification assays for AAV (suspension 293) and lentivirus (adherent 293T) and used DoE statistical methods to optimize up to six different production parameters simultaneously. This approach of combining high-throughput virology assays with DoE is amenable to various vector production platforms, including replicating viruses, reverse genetics and inducible systems. AAV production in suspension HEK293s was optimized, with the methodology allowing simultaneous optimization of up to six factors, including total DNA, nucleic acid type, cell count, transfection reagents, and addition of small molecule enhancers to optimize AAV production. Additionally, LV production optimization in adherent HEK293T by full factorial combination of various plasmid ratios was performed. Importantly, the optimized conditions identified at a small scale were translatable to larger-scale formats, demonstrating scalability. We demonstrate that combining high-throughput assays with the statistical power of DoE enables process optimization of multiple factors in time frames not achievable through traditional OFAT methods. Furthermore, we exemplify how the output data can be used to optimize processes for various factors, including transduction signal, TU/mL, vg/mL or even cost/vg. The flexibility of optimizing different outputs from a single data set unlocks the potential for data-driven decisions to maximize production processes based on strategic needs.