Spline growth modelling is a popular tool to model change processes with distinct phases and change points in longitudinal studies. Focusing on linear spline growth models with two phases and a fixed change point (the transition point from one phase to the other), we detail how to find optimal data collection designs that maximize the efficiency of detecting key parameters in the spline models, holding the total number of data points or sample size constant. We identify efficient designs for the cases where (a) the exact location of the change point is known (complete certainty), (b) only the interval that contains the change point is known (partial certainty), and (c) no prior knowledge on the location of the change point is available (zero certainty). We conclude with recommendations for optimal number and allocation of data collection points.