Not sure I fully understand the problem.
Here goes anyway, if the output is categorical, then you cannot analyse for this in the ANOVA that the analyse factorial design tool is using.
You can analyse for the categorical output using one of the logistic regression tools in the regression menu. There is a problem here in that this model is not stored for the response optimiser and wont be included. The response optimiser only knows about responses analysed with the analyse tools in DOE.
You can store the calculated event probabilities from the logistic regression back in the worksheet, and I suppose you could then put those event probabilities in the analysis, but, I cant see it being a particularly valid analysis as your mixing probabilities and responses. You'll end up with problems where probabilities are close to 1 or 0 with any fit from an Anova or regression.
(not the best explanation but it will do)
The other issue it could be here is that the response optimiser does not work for General full factorial designs.
2 level factorial designs, mixture, response surface, all fit a regression model, and that model is used by the optimiser.
In a general full factorial you are only finding the average response of the level, hence as your only dealing with average of the level even for continous factors the response optimiser, and indeed any of the tools that rely on a fitted regression are not available.
So if your using a general full factorial design for categorical inputs you cannot use the response optimiser. A note is you can use categorical inputs for a 2 level factorial design, but it does fit a regression using coded values of -1 and +1 and it will allow the use of the optimiser. 2 levels is easy, you can fit a straight line to 2 levels.
General full factorial designs though cannot aassume a linear fit through different categorical inputs.
Hope that covers at least some of what you where asking