A new generation of space-based observations has revolutionised the field of asteroseismology. In this thesis, we present the asteroseismic preparations undertaken for a new mission; the NASA TESS satellite. Like its predecessor Kepler, TESS is primarily an exoplanet-hunting satellite. It will observe planetary transits around bright stars. Here, a parametric algorithm was developed to select 'high priority' stars for TESS short cadence observation. These are main sequence, subgiant and red giant stars that will display detectable solar-like oscillations. By observing these stars at a short cadence, TESS can revolutionise both the fields of asteroseismology and exoplanetary science. We also present predictions made about the overlap between these two fields; the numbers of exoplanet-host stars that display solar-like oscillations observed by TESS. In addition, the scaling relations used to construct the algorithm are thoroughly tested and found to be robust. As part of this robustness testing, analytical equations to calculate reliable uncertainties for ∆ν and ν max were also developed. Finally, a machine learning algorithm is developed to select solar-like oscillators using only stellar observables. This algorithm was developed for TESS, but can be easily adapted to automatically perform high priority target selection for any future photometric mission.