Cost-effectiveness models often require the consideration of a sequence of treatments. This enables the downstream implications of a treatment to be captured, and alternative sequences to be compared. However, when many treatments are available, the number of feasible sequences can be large. Also, if the objective is to maximise net benefit for a given ICER threshold, then a comparative analysis to identify the optimal sequence may not be possible. This is further compounded when using individual patient simulation (IPS), because of the increased computational burden compared with cohort approaches. The aim of this study was to undertake a systematic review of optimisation methods that are applicable to a treatment sequencing IPS model. 28 key papers were identified across a range of academic subjects. Metaheuristics including simulated annealing, tabu search and genetic algorithms have been applied to simulation-optimisation problems and a bespoke review framework was applied to determine their appropriateness. Based on the review, a framework for the economic evaluation of treatment sequences was developed. The framework considers the requirements of a cost-effectiveness model to efficiently evaluate sequences, the application of the reviewed metaheuristics to determine the optimal sequence, and the consideration of these results within a decision-making context. This will be applied to a case study in rheumatoid arthritis. Alternative metaheuristic algorithms will be applied in an attempt to estimate a (near) optimal treatment sequence. Preliminary results of these experiments will be available in time for the November 2014 ISPOR conference. If these methods prove successful and feasible, then the framework may have potential applicability to sequencing models in many diseases. Whether there is the capability for it to be applicable within the current process for decision-making organisations such as NICE remains an open question, however, identifying an optimal sequence in a decision problem is of interest to decision makers.