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This paper presents a general framework for optimization of haptic interfaces, in particular for haptic interfaces with closed kinematic chains, with respect to multiple design objectives, namely kinematic and dynamic criteria. Both performance measures are discussed and optimization problems for a haptic interface with best worst-case kinematic and dynamic performance are formulated. Non-convex single objective optimization problems are solved with a branch-and-bound type (culling) algorithm. Pareto methods characterizing the trade-off between multiple design criteria are advocated for multi-criteria optimization over widely used scalarization approaches and Normal Boundary Intersection method is applied to efficiently obtain the Pareto-front hyper-surface. The framework is applied to a sample parallel mechanism (five-bar mechanism) and the results are compared with the results of previously published methods in the literature. Finally, dimensional synthesis of a high performance haptic interface utilizing its Pareto-front curve is demonstrated.