If your theory (or common sense) says that those predictors are relevant to predict you outcome, then add them in the model (regardless of significance of their impact or percent of variance explained). In terms of order, I typically include the focal predictor in the last place (although in practice in hardly ever matters).

Multivariate analysis of variance (MANOVA) is a case when you have several categorical predictors. I don't see how it's relevant here, unless you do have 2+ categorical predictors.

On a side note, I wonder why you choose a binomial regression. What is the underlying logic/justification? This is quite an advanced model, so I am curious what is that your outcome (and its distribution) that you are using such model.