This study developed a cell-based spatial optimization model compatible with the ArcGIS platform, termed Dynamically Dimensioned Search Landscape Optimization Planning model (DDSLOP), for landscape planning. The development of the proposed model was based on the Dynamically Dimensioned Search Algorithm, which can efficiently find an optimal global solution within the massive solution space inherent to multi-dimensional analysis. Therefore, the DDSLOP model can reveal landscape pattern scenarios suited to specific managerial purposes at a cellular level. To evaluate the DDSLOP model, we applied it to a landscape planning initiative that focused on the conservation of three bird species in the National Taiwan University Highland Experimental Farm (NTU-HEF). We compared the proposed model with the Land-Use Pattern Optimization-library (LUPOlib), which was used in the optimization of landscapes at a patch level. The results of the comparison revealed that our fine scale optimization method has better flexibility, and can therefore form landscape structures, which, overall, provides not only better individual habitats for the target species, but also landscape patterns that foster high habitat connectivity, both important aspects of conservation efforts.