Accurate estimates of population-level parameters of parasites, such as prevalence and mean intensity, require large sample sizes. The processing of such samples becomes an overwhelming task when parasites are abundant, as with trematode metacercariae in fishes. In the present study, a subsampling method reduced processing time while maintaining an accurate estimation of metacercariae prevalence and intensity across 3 trematode species and 2 fish species. By double sampling, we generated regression models to predict total intensity from a combination of subsamples. The key to this approach lies in choosing the best strategy from a large number of potential subsampling routines. We selected the most efficient routine by weighing the costs and benefits of each. This approach, however, could not provide an estimate of parasite abundance when no parasites occurred in the initial subsample. To estimate prevalence accurately, our subsampling algorithm prescribed an additional sampling sequence using a new, optimal regression model. In addition, we optimized the technique to measure three parasite species infecting a single host simultaneously. This efficient subsampling procedure decreased the overall processing time per host by up to 91% while obtaining accurate (R² > 0.8) estimates for both prevalence and intensity.