Individual-based data sets tracking organisms over space and time are fundamental to answering broad questions in ecology and evolution. A 'permanent' genetic tag circumvents a need to invasively mark or tag animals, especially if there are little phenotypic differences among individuals. However, genetic tracking of individuals does not come without its limits; correctly matching genotypes and error rates associated with laboratory work can make it difficult to parse out matched individuals. In addition, defining a sampling design that effectively matches individuals in the wild can be a challenge for researchers. Here, we combine the two objectives of defining sampling design and reducing genotyping error through an efficient Python-based computer-modelling program, wisepair. We describe the methods used to develop the computer program and assess its effectiveness through three empirical data sets, with and without reference genotypes. Our results show that wisepair outperformed similar genotype matching programs using previously published from reference genotype data of diurnal poison frogs (Allobates femoralis) and without-reference (faecal) genotype sample data sets of harbour seals (Phoca vitulina) and Eurasian otters (Lutra lutra). In addition, due to limited sampling effort in the harbour seal data, we present optimal sampling designs for future projects. wisepair allows for minimal sacrifice in the available methods as it incorporates sample rerun error data, allelic pairwise comparisons and probabilistic simulations to determine matching thresholds. Our program is the lone tool available to researchers to define parameters a priori for genetic tracking studies.