Genotype-by-sequencing (GBS) methods have revolutionized the field of molecular ecology, but their application in molecular phylogenetics remains somewhat limited. In addition, most phylogenetic studies based on large GBS data sets have relied on analyses of concatenated data rather than species tree methods that explicitly account for genealogical stochasticity among loci. We explored the utility of “double-digest” restriction site-associated DNA sequencing (ddRAD-seq) for phylogenetic analyses of the Lagonosticta firefinches (family Estrildidae) and the Vidua brood parasitic finches (family Viduidae). As expected, the number of homologous loci shared among samples was negatively correlated with genetic distance due to the accumulation of restriction site polymorphisms. Nonetheless, for each genus, we obtained data sets of ∼3000 loci shared in common among all samples, including a more distantly related outgroup taxon. For all samples combined, we obtained >1000 homologous loci despite ∼20 my divergence between estrildid and parasitic finches. In addition to nucleotide polymorphisms, the ddRAD-seq data yielded large sets of indel and locus presence–absence polymorphisms, all of which had higher consistency indices than mtDNA sequence data in the context of concatenated parsimony analyses. Species tree methods, using individual gene trees or single nucleotide polymorphisms as input, generated results broadly consistent with analyses of concatenated data, particularly for Lagonosticta, which appears to have a well resolved, bifurcating history. Results for Vidua were also generally consistent across methods and data sets, although nodal support and results from different species tree methods were more variable. Lower gene tree congruence in Vidua is likely the result of its unique evolutionary history, which includes rapid speciation by host shift and occasional hybridization and introgression due to incomplete reproductive isolation. We conclude that ddRAD-seq is a cost-effective method for generating robust phylogenetic data sets, particularly for analyses of closely related species and genera.