This is a complicated thing to do, but might be possible. Two approaches come to my mind: cross-concept prohibitions and what is called the "Premium Pricing" approach.
The first thing to do is to create your experiment without any prohibitions and use the Test Design feature of the software to create robotic respondents who answer randomly, then to estimate aggregate logit effects and report the standard errors (the precision) of the attribute levels. You are hoping for standard errors for each of your attribute levels of 0.05 or less. This is your standard. And, when you try to constrain your design to follow the patterns of more realistic tradeoffs you are trying to implement, you'll want to compare the results to this "no-prohibitions" standard.
If you have our advanced design module for CBC software, there is an advanced feature in the prohibitions area that allows you to input cross-concept prohibitions. That is to say, you can prohibit a combination of attribute levels from occurring if another combination of attribute levels occurs for a different concept within the SAME choice task. Try this, then run the Test Design feature again with the same number of robotic respondents as before. Compare the standard errors. You should hope that they don't increase very much. And, you should hope that they are all still under 0.05 for each attribute level.
Sometimes when you are trying to implement so many prohibitions, the Balanced Overlap, Complete Enumeration, and Shortcut design methods cannot find a solution and report that there are "too many prohibitions." This is a sign that you are in a danger zone. But, you can push forward investigating whether it might be possible to do these prohibitions using the "Random" design approach. Again, test the design and examine your standard errors carefully.
If this doesn't work, then another approach to investigate that often works better is the "Premium Pricing" approach, which is not documented in our software help. It is an advanced strategy that has been taught at our Turbo CBC seminars by a consultant named David Lyon, from Aurora Market Modeling. We have described the Premium Pricing approach in our new book, "Becoming an Expert in Conjoint Analysis" in chapter 3.
If you try the premium pricing approach, you should again create robotic dummy respondents and create an aggregate logit model to test the precision of your effects.
Even though I describe using aggregate logit to test your models, you will likely do the final utility estimation using something like HB.