1) As a good starting point, you probably want to run your counts analysis and see how many things respondents were saying would work for them in the screener section. The screener section is essentially coded up like a choice between the profile shown and the none option. If your respondents are rejecting a majority of the screening concepts, then it would seem correct that you would have a high none utility.
2) Typically constraints are a good idea when using many breakpoints as individual respondents may have not had much data within your breakpoints, especially if they are very picky during the screener section. As price increases, we would expect utility to decrease, so the slope should have a negative direction.
3) This one is a bit trickier to speculate on, as #1 and #2 might have a big impact on your simulations (if only 1/3 of the people are choosing something and everyone else chooses none, then they just might simply be price insensitive, but it could also be your model is misbehaving a bit from #2)