Increasing photovoltaic (PV) installed capacity is considered a primary way to achieve carbon neutrality targets. However, the rapid development has led to the PV market saturation in some areas, and solar curtailment has even occurred due to the oversupply. With more demand for PV installation in the future, the question of where to efficiently deploy PV systems to both meet the increasing PV electricity demand and alleviate the mismatch between PV supply and local demand becomes relevant. To explore these issues, this paper proposes an integrated framework for planning the PV installed capacity allocation. The framework can be decomposed into two stages: at the optimization stage, a multi-objective optimization (MOO) model is proposed, integrating the minimization of mismatch between PV supply and local demand as an additional objective along with the traditional economic and environmental profits maximization objective; and at the evaluation stage, an entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) is employed to make the decision of the PV allocation scheme. By applying this integrated model to the Chinese PV deployment, the results reveal that the consideration of the match between PV supply and local demand has a direct influence on the PV installation layout. More than 50% of the PV systems are expected to be in areas with higher demand and higher profits, rather than the areas with high solar irradiance but lower demand. Distributed PV installations are expected to account for 60% of the total installed capacity. The implementation of this PV installation planning is estimated to result in a reduction of 1036 Mt CO₂ emission, a 14.2% PV penetration rate, and 566 billion CNY total profits, making it an attractive and feasible instrument for countries striving towards carbon neutrality goals.