Abstract This investigation is mainly focused on the LSST Survey Strategy Optimization process, a bottom-up approach that turned out to be quite effective in involving the scientific community in the definition of the LSST observing strategy. We are mainly interested in using radial variables (RR Lyrae, classical Cepheids, long-period variables) as stellar tracers and distance indicators, and we developed a new tool called PulsationStarRecovery to quantify the recovery of the light-curve period and amplitude from an LSST-simulated time series. The outputs of this code are pulsation parameters (period, amplitude, mean magnitude) together with quantitative information concerning the difference between the shape of the light curve and template light curves. Furthermore, we apply the newborn metric to simulate LSST observations and recovery of different types of pulsating stars hosted by selected massive stellar systems (19 Local Group dwarf galaxies and the Large Magellanic Cloud) to show how the recovery changes according to distance and variable-star type. We show that this exercise is essential to understand the potential of LSST in this field since excellent recovery is necessary to optimize the use of predicted period–luminosity, period–amplitude, and color–color relations to constrain the cosmic distance scale and the metallicity distribution function of different stellar populations.