Recent improvements in adder optimization could be achieved by optimizing the AND-trees occurring within the constructed circuits. The overlap of such trees and its potential for pure size optimization has not been taken into account though. Motivated by this, we examine the fundamental problem of minimizing the size of a circuit for multiple AND-functions on intersecting variable sets. Our formulation generalizes the overlapping \AND-trees within adder optimization but is in NP, in contrast to general Boolean circuit optimization which is in $Σ_2^p$ (and thus suspected not to be in NP). While restructuring the AND- or XOR-trees simultaneously, we optimize the total number of gates needed for all functions to be computed. We show that this problem is APX-hard already for functions of few variables and present efficient approximation algorithms for the case in which the Boolean functions depend on at most 3 or 4 variables each, achieving guarantees of $\frac 43$ and $1.9$, respectively. To conclude, we give a polynomial approximation algorithm with guarantee $\frac 23k$ for AND-functions of up to $k$ variables. To achieve these results, the key technique is to determine how much overlap among the variable sets makes tree construction cheap and how little makes the optimum solution large.