Localization awareness is a fundamental requirement in many Internet of Things (IoT) and other wireless sensor applications. The information transmitted by an individual entity or node is of limited use without the knowledge of its location. Research in this area is mostly geared towards multi-hop range-free localization algorithm as that only utilizes connectivity (neighbors) information. This work focuses on anchor-based, range-free localization algorithm, particularly in anisotropic networks. We observe that the pioneer Distance Vector Hop or DV-Hop algorithm, which provides accurate estimation in isotropic networks, can be enhanced to compute localization estimation for anisotropic networks with similar or comparable accuracy. The recently proposed algorithms for anisotropic networks are complex with communication and computational overheads. These algorithms may also be overkill for several location dependent protocols and applications. This paper proposes a scheme, called DV-maxHop, which reaches comparable accuracy quickly utilizing simpler, practical and proven variant of the DV-Hop algorithm. We evaluate the performance of our scheme using extensive simulation on several topologies under the effect of multiple anisotropic factors such as the existence of obstacles, sparse and non-uniform sensor distribution, and irregular radio propagation pattern. Even for isotropic networks, our scheme out-performed recent algorithms with lower computational overheads as well as reduced energy or communication cost due to its faster convergence. We also introduce the formulation and simulation of Multi-objective Optimization to obtain the optimal solution.