Routine testing for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) in health care workers (HCWs) is critical. Group testing strategies to increase capacity facilitate mass population testing but do not prioritize turnaround time, an important consideration for HCW screening. We propose a nonadaptive combinatorial (NAC) group testing strategy to increase throughput while facilitating rapid turnaround. NAC matrices were constructed for sample sizes of 700, 350, and 250. Matrix performance was tested by simulation under different SARS-CoV-2 prevalence scenarios of 0.1% to 10%. NAC matrices were compared versus Dorfman sequential (DS) group testing approaches. NAC matrices performed well at low prevalence levels, with an average of 97% of samples resolved after a single round of testing via the n = 700 matrix at a prevalence of 1%. In simulations of low to medium (0.1% to 3%) prevalence, all NAC matrices were superior to the DS strategy, measured by fewer repeated tests required. At very high prevalence levels (10%), the DS matrix was marginally superior, although both group testing approaches performed poorly at high prevalence levels. This strategy maximizes the proportion of samples resolved after a single round of testing, allowing prompt return of results to HCWs. This methodology may allow laboratories to adapt their testing scheme based on required throughput and the current population prevalence, facilitating a data-driven testing strategy.