Future manned space missions are expected to greatly increase the space vehicle's size, weight, and heat dissipation requirements. An effective means to reducing both size and weight is to replace single-phase thermal management systems with two-phase counterparts that capitalize upon both latent and sensible heat of the coolant rather than sensible heat alone. This shift is expected to yield orders of magnitude enhancements in flow boiling and condensation heat transfer coefficients. A major challenge to this shift is a lack of reliable tools for accurate prediction of two-phase pressure drop and heat transfer coefficient in reduced gravity. Developing such tools will require a sophisticated experimental facility to enable investigators to perform both flow boiling and condensation experiments in microgravity in pursuit of reliable databases. This study will discuss the development of the Flow Boiling and Condensation Experiment (FBCE) for the International Space Station (ISS), which was initiated in 2012 in collaboration between Purdue University and NASA Glenn Research Center. This facility was recently tested in parabolic flight to acquire condensation data for FC-72 in microgravity, aided by high-speed video analysis of interfacial structure of the condensation film. The condensation is achieved by rejecting heat to a counter flow of water, and experiments were performed at different mass velocities of FC-72 and water and different FC-72 inlet qualities. It is shown that the film flow varies from smooth-laminar to wavy-laminar and ultimately turbulent with increasing FC-72 mass velocity. The heat transfer coefficient is highest near the inlet of the condensation tube, where the film is thinnest, and decreases monotonically along the tube, except for high FC-72 mass velocities, where the heat transfer coefficient is enhanced downstream. This enhancement is attributed to both turbulence and increased interfacial waviness. One-ge correlations are shown to predict the average condensation heat transfer coefficient with varying degrees of success, and a recent correlation is identified for its superior predictive capability, evidenced by a mean absolute error of 21.7%.