Generally speaking, $R^2$ always increases as you increase the number of variables in your model, so by itself it is not a good criterion to know when you should stop adding variables. Instead you should use a different number that in some sense measures that "higher $R^2$ is better, but you don't want too many variables either". Quantities that measure this are the [adjusted $R^2$](https://en.wikipedia.org/wiki/Coefficient_of_determination#Adjusted_R2), the [AIC](https://en.wikipedia.org/wiki/Akaike_information_criterion), and the [BIC](https://en.wikipedia.org/wiki/Bayesian_information_criterion).

In your specific situation, since you have two variables with moderately high correlation, I would also try some sort of dimensionality reduction algorithm, and they try a linear regression on just two variables. I would look into [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) for its simplicity.