How would you model the evolution of two genotypes across generations?
So, what’s a way of modeling how resultant mutations might occur, such that B has a chance of producing some offspring that are fitter than A’s offspring?


Here's one possible solution that fits your criteria. You could have something like this (with "d" being the distance between "x" and "y", i.e. d=x-y and "n" being the n-th offspring, so it varies between 1 and x for A and 1 and y for B) :

A: n -> x + r(n), where r(n) is a random number between x-d and x+d
B: n -> y + r(n), where r(n) is a random number between y-d and y+d

(I write r(n) not because r is a function n, but because a different number is drawn for each n. I also didn't specify r's distribution, that would also need to be decided when making an actual model)  

This is your situation where some offspring of B have a chance of being fitter than offspring of A, and the least-fit offspring of B can even be fitter than the fittest offspring of A. I think it fits your criteria and thus answers your question. The sum of the grandoffspring is basically the basic x or y squared (that you would get if each offspring had the same fitness as their parent) + the sum of all the "r"s.