Modern computational methods using patient Human Immunodeficiency Virus type 1 (HIV-1) genetic sequences can model population-wide viral transmission dynamics. Accurate transmission inferences can play a critical role in the characterization of high-risk transmission clusters important for enhanced epidemiological control. We evaluated a phylogenetics-based analysis pipeline to infer person-to-person (P2P) infection dates and transmission relationships using 139 patient HIV-1 polymerase Sanger sequences curated by the Southern Alberta HIV Clinic. Parameter combinations tailored to HIV-1 transmissions were tuned with respect to inference accuracy. Inference accuracy was assessed using clinically confirmed P2P transmission patient data. The most accurate parameter settings correctly inferred 48.56% of the P2P relationships (95% confidence interval 63.89–33.33%), slightly lower than next-generation-sequencing methods. The infection date was correctly inferred 43.02% (95% confidence interval 49.89–35.63%). Several novel unsuspected transmission clusters of up to twelve patients were identified. An accuracy trade-off between inferring transmission relationships and infection dates was observed. Using clinically confirmed P2P transmission data as benchmark, our phylogenetic methods identified sufficient P2P transmission relationships using readily available low-resolution Sanger sequences. These approaches may give valuable information about HIV infection dynamics within a population and may be easily deployed to guide public health interventions, without a need for next generation sequencing technology.