OPTIF9 can be converted to C (from Fortran) and may already have been by somebody.
If what you mean by box constraints is that it supports upper and lower limits on parameter values, I believe there is a version that does that.
That is a tricky problem, because it means whenever a parameter gets to a boundary, it effectively reduces the degrees of freedom by 1.
It can get "stuck on a wall" when you didn't really want it to.
What we've found is that it's better to use an unconstrained minimizer and transform parameters, via something like a log or logit transform, so that in the search space they are unconstrained, but in the model space they are constrained.
As far as the other types of constraints, I don't know, although one option is, as part of your objective function, to make it get really bad when constraints are violated, so the optimizer avoids those areas.
I've found when I have a really flexible set of constraints, if I want a good trouble-free algorithm, I use Metropolis-Hastings.
Unless I'm wrong, if it generates a sample that violates constraints, you can simply discard the sample.
It takes longer, but it's simple and always works.
