Evolutionary Multi-Objective Optimization (MOO) is typically employed when a decision maker wants to analyse the trade-offs involved between multiple conflicting objectives. It has the advantage of yielding a set of Pareto-optimal or equally-good solutions in a single run. However, this approach is often criticized for being extremely time consuming for chemical engineering applications. To a large extent, the solution time for evolutionary MOO depends on the computational complexity of objective functions (and constraints); the higher the computational complexity, the greater would be the time required to get to the final Pareto-optimal solution set. This is because evolutionary algorithms need to perform a large number of objective function evaluations. A significant amount of time could however be saved by replacing the computationally expensive objective functions with their cheaper approximations, known as surrogates or response surface models in an optimization framework known as surrogate-assisted MOO. This chapter compares two existing surrogate-assisted MOO strategies using two test problems from Chapter 5. Then, the computationally intensive optimization of a complex flowsheet simulation of a coal to ammonia process involving Carbon Capture and Sequestration (CCS) is solved with the modified Multiple Adaptive Spatially Distributed Surrogates (MASDS) algorithm. The results obtained by this algorithm are compared with those obtained from the Business-As-Usual (BAU) approach which evaluates objective functions directly without any surrogate-assistance. Results show significant savings, measured in terms of the hypervolume spanned by the Pareto-front obtained from the two approaches, for a fixed computation budget. The surrogate-assisted strategy thus allows for better integration of computationally complex units into large-scale plant simulations. It also yields an array of surrogates, which can be used for any future prediction of the objective function values.