Dynamic multi-objective optimisation problems (DMOPs) are dynamic in nature where at least two objectives are in conflict with one another and the objectives and/or constraints change over time. Dynamic multi-objective optimisation algorithms (DMOAs) have to find solutions in a short time before the environment changes, after which the found solutions may not be optimal anymore. Before applying a DMOA to real-world problems it is important to understand the behaviour of the algorithm under various environment changes and when irregular changes occur. Typically plots of the DMOA's found trade-off solutions or Pareto-optimal fronts (POFs) and their corresponding performance measure values are plotted over time. However, this makes it difficult to analyse the behaviour of the DMOA's population during the search process and especially after changes occurred. This paper proposes a visualisation approach to assist with the analysis of a DMOA's behaviour during the search by incorporating the found POF, the reference POF, performance measure values and when changes occur into one plot that can be viewed for each iteration of the search process.