Identifying optimal refurbishment strategies for a building stock is a challenging task. Numerous energy efficiency measures can be undertaken for each building. In addition, under financial constraints, real estate owners have to decide in which buildings and renovation actions to invest first, as well as how to plan renovations over time. A methodology combining multi-objective optimisation (MOO) with dynamic building energy simulation is proposed to help building stock managers to set their priorities. The MOO is based on the NSGA-II algorithm: a first chromosome represents the possible buildings and the actions to invest in, while a second chromosome corresponds to the temporal sequencing of these retrofit choices. Finally, optimal refurbishment schedules can be identified. The methodology is applied to multi-dwelling units of a portfolio property owner in Greater Paris Area. Refurbishment schedules, minimising investment cost and energy consumption, and meeting yearly budget constraints, were analysed. Among optimal schedules, actions that are frequently selected on a building during the first few years of renovation are identified as priorities. The developed methodology, implemented in a tool, thus provides a first level of decision aid. Acronyms: AHP, Analytic Hierarchy Process; B, Large apartment block; CAPEX, CAPital EXpenditure; D1, District 1; D2, District 2; DHW, Domestic Hot Water; DBES, Dynamic Building Energy Simulation; E-W, East-West Orientation; GA, Genetic Algorithm; GHG, Greenhouse Gases; LCC, Life Cycle Cost; MACBETH, Measuring Attractiveness by a Categorical Based Evaluation Technique; MCDM, Multi-Criteria Decision Making; MILP, Mixed-Integer Linear Programing; MOO, Multi-Objective Optimisation; N-S, North-South Orientation; NPV, Net Present Value; OPEX, OPerating EXpenditure; PSO, Particle Swarm Optimisation; SA, Sensitivity Analysis; T, Tower; UA, Uncertainty Analysis.< Leer menos