Wind power generation represents one of the main resources of modern renewable power systems. Its deployment is governed by numerous factors, e.g., weather patterns, land form, electric infrastructure availability and sizing, and cost-effectiveness. In this work, we propose a model for the optimal wind farm and turbine placement considering the geographical features, generating capacity, transmission infrastructure, and cost. We model these key considerations in addition to the location-specific weather using a geospatial information system (GIS). Our proposed method relies on a two-layer coordinate system which is compatible with decomposition methods. The upper layer accounts for wind farm planning and considers, for example, power converters, substations, and transmission lines. The lower layer accounts for wind turbine placement and generation sizing within a wind farm. Our formulation aims to maximize the expected power generation while balancing generation variance and infrastructure cost. The problem is expressed as a mixed-integer convex quadratic program (MICQP) and readily solved with an off-the-shelf commercial solver. The results across a numerical case study in Québec, Canada illustrates the effectiveness of the proposed method.