This talk will focus on incorporating nonlinear constraints in both the design and analyses of experiments. JMP® allows us to constrain the experimental design space with disallowed combinations, but it does not currently obey those constraints for optimization. Working with JMP® Profilers, a method will be presented that incorporates nonlinear constraints in optimization problems in the form of disallowed combinations. Multiple examples will be shown that address both design and optimization. All data and the JMP® journal used in the presentation will be made freely available.