How to optimize a utility function that contains step function?

I have an optimization problem with an uncommon utility: to find a $beta$ that maximizes

$$
r^{T}cdot H(Xcdotbeta)
$$

where $H()$ is a Heaviside step function as in wiki

$r$ is a vector of size 1000

$X$ is a 1000×50 "tall" matrix

$beta$ is a vector of size 50

I am familiar with gradient descent, which is how I usually solve an optimization problem. But Heaviside function does not work with gradient descent. So I am wondering if anyone here could shed some light on how to solve such optimization problem.