We consider the problem of tracking multiple, partially observed targets using multiple sensors arranged in a given configuration. We model the problem as a special case of a (finite horizon) DEC-POMDP. We present a quadratic program whose globally optimal solution yields an optimal tracking joint policy, one that maximizes the expected targets detected over the given horizon. However, a globally optimal solution to the QP cannot always be found since the QP is nonconvex. To remedy this, we present two linearizations of the QP to equivalent 0-1 mixed integer linear programs (MIPs) whose optimal solutions, which may be always found through the branch and bound method, for example, yield optimal joint policies. Computational experience on different sensor configurations shows that finding an optimal joint policy by solving the proposed MIPs is much faster than using existing algorithms for the problem.