For many kinds of vehicle routing problems (VRPs), a popular heuristic approach involves constructing a Traveling Salesman Problem (TSP) solution, referred to as a long tour, then partitioning segments of the solution into routes for different vehicles with respect to problem constraints. Previously, a Split algorithm with a worst-case runtime of $Θ(n)$ was proposed for the capacitated VRP (CVRP) that finds the most cost-efficient partition of customers, given a long tour. This was an improvement over the previously fastest-known Split algorithm with a worst-case runtime of $Θ(n^2)$ that was based on Bellman's shortest path algorithm. While this linear Split has been an integral part of modern state-of-the-art CVRP approaches, little progress has been made in extending this algorithm to handle additional VRP variants, limiting the general applicability of the algorithm. In this work, we propose an extension of the linear Split that handles two cardinal VRP variants simultaneously: (i) simultaneous pickups and deliveries (VRPSPD) and (ii) time windows (VRPTW). The resulting $Θ(n)$ algorithm is guaranteed to be optimal, assuming travel times between nodes satisfy the triangle inequality. Additionally, we extend the linear Split to handle a capacity penalty for the VRPSPD. For the VRPTW, we extend the linear Split to handle the CVRP capacity penalty in conjunction with the popular time warp penalty function. Computational experiments are performed to empirically validate the speed gains of these linear Splits against their $Θ$($n^2$) counterparts.