Rapidly developing sequencing technologies and declining costs have made it possible to collect genome-scale data from population-level samples in non-model systems. Inferential tools for historical demography given these datasets are, at present, underdeveloped. In particular, approximate Bayesian computation (ABC) has yet to be widely embraced by researchers generating these data. Here, we demonstrate the promise of ABC for analysis of the large datasets that are now attainable from non-model taxa through current genomic sequencing technologies. We develop and test an ABC framework for model selection and parameter estimation given histories of three-population divergence with admixture. We then explore different sampling regimes to illustrate how sampling more loci, longer loci, or more individuals affects the quality of model selection and parameter estimation in this ABC framework. Our results show that inferences improved substantially with increases in the number and/or length of sequenced loci, while less benefit was gained by sampling large numbers of individuals. Optimal sampling strategies given our inferential models included at least 2000 loci, each approximately 2kb in length, sampled from five diploid individuals per population, although specific strategies are model- and question-dependent. We tested our ABC approach through simulation-based cross-validations and illustrate its application using previously analyzed data from the oak gall wasp, Biorhiza pallida.