Many processes are described by differential equations and their optimisation involves the determination of optimal time-varying control policies. Moreover, in view of sustainable development multiple and often conflicting objectives arise. For instance, not only more profitable (economically sustainable) but also cleaner (environmentally sustainable) and safer (socially sustainable) processes are aimed at. To enhance decision making in practice (and even allow real-time decision making), it is important to enable (i) fast and accurate generation of optimal alternatives (Pareto optimal solutions) and (ii) evaluation of the trade-offs between them. To achieve this aim a tight integration is needed between efficient multi-objective scalarisation strategies (e.g., Normal Boundary Intersection and (Enhanced) Normalised Normal Constraint) and fast deterministic gradient-based dynamic optimisation approaches (e.g., Single/Multiple Shooting or Collocation). Some recent developments exploiting this integration include (i) robust multi-objective optimal control, (ii) multi-objective mixed-integer optimal control, (iii) multi-objective dynamic optimal experiment design, (iv) multi-objective optimisation of periodic distributed parameter systems and (v) multi-objective tuning of NMPC controllers. For each of the cases an illustration is available. Acknowledgements KUL: OT/10/035, PFV/10/002, KP/09/005; FWO: G.0930.13, 1.5.189.13N; BelSPO: IAP PVII/19, IWT-SB: 101048, 101643, 101080.