Crown clade Apocynaceae comprise seven primary lineages of lianas, shrubs, and herbs with a diversity of pollen aggregation morphologies including monads, tetrads, and pollinia, making them an ideal group for investigating the evolution and function of pollen packaging. Traditional molecular systematic approaches utilizing small amounts of sequence data have failed to resolve relationships along the spine of the crown clade, a likely ancient rapid radiation. The previous best estimate of the phylogeny was a five-way polytomy, leaving ambiguous the homology of aggregated pollen in two major lineages, the Periplocoideae, which possess pollen tetrads, and the milkweeds (Secamonoideae plus Asclepiadoideae), which possess pollinia. To assess whether greatly increased character sampling would resolve these relationships, a plastome sequence data matrix was assembled for 13 taxa of Apocynaceae, including nine newly generated complete plastomes, one partial new plastome, and three previously reported plastomes, collectively representing all primary crown clade lineages and outgroups. The effects of phylogenetic noise, long-branch attraction, and model selection (linked versus unlinked branch lengths among data partitions) were evaluated in a hypothesis-testing framework based on Shimodaira–Hasegawa tests. Discrimination among alternative crown clade resolutions was affected by all three factors. Exclusion of the noisiest alignment positions and topologies influenced by long-branch attraction resulted in a trichotomy along the spine of the crown clade consisting of Rhabdadenia + the Asian clade, Baisseeae + milkweeds, and Periplocoideae + the New World clade. Parsimony reconstruction on all optimal topologies after noise exclusion unambiguously supports parallel evolution of aggregated pollen in Periplocoideae (tetrads) and milkweeds (pollinia). Our phylogenomic approach has greatly advanced the resolution of one of the most perplexing radiations in Apocynaceae, providing the basis for study of convergent floral morphologies and their adaptive value.