Freeway　congestion　has　become　a　serious　problem　for　city　manager　due　to　the　continuous　growth　of　traffic　demand.　Different　kinds　of　traffic　control　management　measures,　such　as　variable　speed　limits　and　ramp　metering,　have　been　proposed　and　partly　implemented　to　handle　this　problem.　Mobility,　safety　and　emission　are　used　as　the　control　objectives.　This　paper　investigates　multi-objective　optimization　with　the　coordinated　control　of　variable　speed　limits　and　ramp　metering.　Moreover,　model　predictive　control　is　used　as　the　control　framework　in　a　rolling　horizon　system.　Furthermore,　multi-objective　particle　swarm　optimal　algorithm　is　adopted　to　acquire　the　best　control　signal　for　mobility,　safety　and　emission　performance.　The　proposed　method　is　evaluated　through　simulation　and　calibrated　using　the　real-world　data　at　a　freeway　stretch.　The　experiment　result　indicates　that　the　mobility　of　the　study　network　was　improved,　and　the　collision　risk　and　carbon　emission　are　reduced　by　the　integration　of　variable　speed　limits　and　ramp　metering.　©　2019　IEEE.