What question are you trying to answer?
If you want an overall test of anything going on, The null is that both main effects and the interaction are all 0, then you can replace all the data points with their ranks and just do a regular ANOVA to compare against an intercept/grand mean only model. This is basically how many of the non-parametric tests work, using the ranks transforms the data to a uniform distribution (under the null) and you get a good approximation by treating it as normal (the Central Limit Theorem applies for the uniform for sample sizes above about 5 or 6).
For other questions you could use permutation tests. If you want to test one of the main effects and the interaction together (but allow the other main effect to be non-zero) then you can permute the predictor being tested. I you want to test the interaction while allowing both main effects to be non-zero then you can fit the reduced model of main-effects only and compute the fitted values and residuals, then randomly permute the residuals and add the permuted residuals back to the fitted values and fit the full anova model including the interaction. Repeat this a bunch of times to get the null distribution for the size of the interaction effect to compare with the size of the interaction effect from the original data.
There may be existing SAS code for doing things like this, I have seen some basic tutorials on using SAS for bootstrap and permutation tests (the quickest way seems to be using the data step to create all the datasets in one big table, then using by processing to do the analyses). Personally I use R for this type of thing so can't be of more help in using SAS.
