In optimal input design problems, the designer seeks to solve for maximally informative inputs to be used as perturbation signals in system identification experiments. Plantfriendly identification experiments are those that satisfy plant or operator constraints on experiment time, input and output amplitudes or input move sizes. These have been reported to be in direct conflict with requirements for good identification. Hence plant-friendly input design is inherently multi-objective in nature. In this contribution, we present the use of two well known techniques of multi-objective optimization to solve for a plant friendly input design where the plant friendly objective is to keep input move sizes low. We relax the constraint on the input move sizes by constraining the variance of the move size instead. Both techniques result in convex optimization problems which can be solved efficiently using powerful algorithms. ©2008 AACC.